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The evolution of the Bitcoin economy: extracting and analyzing the network of payment relationships

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Purpose In this paper, we gather together the minimum units of users' identity in the Bitcoin network (i.e., the individual Bitcoin addresses), and group them into representations of business entities, what we call “super clusters”. While these clusters can remain largely anonymous, we are able to ascribe many of them to particular business categories by analyzing some of their specific transaction patterns, as observed during the period from 2009-2015. We are then able to extract and create a map of the network of payment relationships among them, and analyze transaction behavior found in each business category. We conclude by identifying three marked regimes that have evolved as the Bitcoin economy has grown and matured: from an early prototype stage; to a second growth stage populated in large part with “sin” enterprise (i.e., gambling, black markets); to a third stage marked by a sharp progression away from “sin” and toward legitimate enterprises. Design/methodology/approach Data Mining Findings Key Findings: • Four primary business categories are identified in the Bitcoin economy: Miners, Gambling services, Black Markets and Exchanges. • Common patterns of transaction behaviour between the business categories and their users: • A “one-day” holding period for bitcoin transactions is somewhat typical. That is, a one-day effect where traders, gamblers, black market participants and miners tend to cash out on a daily basis. • There seems to be a strong preference to do business within the bitcoin economy in round lot amounts, whether it is more typical of traders exchanging for fiat money, gamblers placing bets or black market goods being bought and sold. • Distinct patterns of transaction behaviour among the business categories and their users: • Flows between traders and exchanges average just around 20 BTC, and traders buy or sell on average every 11days. Meanwhile, gamblers wager just 0.5 BTC on average, but re-bet often within the same day. • Three marked regimes have evolved as the Bitcoin economy has grown and matured: 1) from an early prototype stage; 2) to a second growth stage populated in large part with “sin” enterprises (i.e., gambling, black markets); 3) to a third stage marked by a sharp progression away from “sin” and towards legitimate enterprises. This evolution of the Bitcoin economy suggests a trend towards legitimate commerce Originality/value We propose a new theoretical framework that allows to investigate and explore the network of payment relationships in the Bitcoin economy. Our study starts by gathering together the minimum units of Bitcoin identities (the individual addresses), and it goes forward in grouping them into approximations of business entities, what we call “super clusters”, by using tested techniques from the literature. A super cluster can be thought of as an approximation of a business entity in that it describes a number of individual addresses that are owned or controlled collectively by the same beneficial owner for some special economic purposes. The majority of these important clusters are initially unknown and uncategorized. The novelty of our study is given by the Pure User Group (PUG) and the Transaction Pattern (TP) analyses, by means of which we are able to ascribe the super clusters into specific business categories and outline a map of the network of payment relationships among them.
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DIGITAL CURRENCIES
The evolution of the
bitcoin economy
Extracting and analyzing the network of
payment relationships
Paolo Tasca
University College London, London, UK
Adam Hayes
University of Wisconsin Madison, Madison, Wisconsin, USA, and
Shaowen Liu
Deutsche Bundesbank, Frankfurt, Germany
Abstract
Purpose This paper aims to gather together the minimum units of usersidentity in the Bitcoin network (i.e.
the individual Bitcoin addresses) and group them into representations of business entities, what we call super
clusters. While these clusters can remain largely anonymous, the authors are able to ascribe many of them to
particular business categories by analyzing some of their specic transaction patterns (TPs), as observed during
the period from 2009 to 2015. The authors are then able to extract and create a map of the network of payment
relationships among them, and analyze transaction behavior found in each business category. They conclude by
identifying three marked regimes that have evolved as the Bitcoin economy has grown and matured: from an
early prototype stage; to a second growth stage populated in large part with sinenterprise (i.e. gambling, black
markets); to a third stage marked by a sharp progression away from sinand toward legitimate enterprises.
Design/methodology/approach Data mining.
Findings Four primary business categories are identied in the Bitcoin economy: miners, gambling
services, black markets and exchanges. Common patterns of transaction behavior between the business
categories and their users are a one-dayholding period for bitcoin transactions is somewhat typical. That is,
a one-day effect where traders, gamblers, black market participants and miners tend to cash out on a daily
basis. There seems to be a strong preference to do business within the bitcoin economy in round lot amounts,
whether it is more typical of traders exchanging for at money, gamblers placing bets or black market goods
being bought and sold. Distinct patterns of transaction behavior among the business categories and their users
are ows between traders and exchanges average just around 20 BTC, and traders buy or sell on average
every 11 days. Meanwhile, gamblers wager just 0.5 BTC on average, but re-bet often within the same day.
Three marked regimes have evolved, as the Bitcoin economy has grown and matured: from an early prototype
stage, to a second growth stage populated in large part with sinenterprises (i.e. gambling, black markets), to
a third stage marked by a sharp progression away from sinand toward legitimate enterprises. This
evolution of the Bitcoin economy suggests a trend toward legitimate commerce.
Originality/value The authors propose a new theoretical framework that allows investigating and
exploring the network of payment relationships in the Bitcoin economy. This study starts by gathering
together the minimum units of Bitcoin identities (the individual addresses), and it goes forward in grouping
JEL classication E42, L14, O12, O35, P40
This paper forms part of a special section Digital currencies, guest edited by Paolo Tasca.
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Received 20 March 2017
Revised 19 October2017
Accepted 16 November2017
The Journal of Risk Finance
Vol. 19 No. 2, 2018
pp. 94-126
© Emerald Publishing Limited
1526-5943
DOI 10.1108/JRF-03-2017-0059
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1526-5943.htm
them into approximations of business entities, what is called super clusters, by using tested techniques from
the literature. A super cluster can be thought of as an approximation of a business entity in that it describes a
number of individual addresses that are owned or controlled collectively by the same benecial owner for
some special economic purposes. The majority of these important clusters are initially unknown and
uncategorized. The novelty of this study is given by the pure user group and the TP analyses, by means of
which the authors are able to ascribe the super clusters into specic business categories and outline a map of
the network of payment relationships among them.
Keywords Business analysis, Bitcoin, Network theory, Blockchain, Digital currencies,
Payment traceability
Paper type Research paper
1. Introduction
As the price of Bitcoin has risen above $4,000 recently, the worlds largest and most used
decentralized cryptocurrency has become a topic of interest among a variety of disciplines
from economics, computer science and payments to public policy, information systems and the
law[1]. No longer a curio for hobbyists, Bitcoin is now being taken quite seriously by academics
and practitioners around the globe. Even central banks such as the Federal Reserve and Bank
of England have started to take notice. Indeed, the cumulative value of all bitcoins has risen
above $70bn, with the number of transactions on the Bitcoin blockchain rising exponentially
from around 1,000 per day in 2011 to more than 300,000 per day at the moment of writing. At
the current exchange rate the notional value of daily turnover approaches $1bn[2].
With a surge in both user base and interest from the outside, understanding what goes
on within the Bitcoin economy makes an important contribution. For example, Bitcoin still
carries a negative connotation among some who associate the cryptocurrency with illegal
activity, made prescient for instance by the take-down of the online black market Silk Road
by the FBI in late 2013. What our analysis shows is that by 2015 such illicit exchange made
up only a very small proportion of all Bitcoin activity. This does not mean that black market
activity has gone away, rather their users and operators have shifted to alternate digital
currencies such as ZCash and Monero, as Bitcoin has matured into a legitimate nancial
institution. At the same time, investors are now seeking to add Bitcoin to their portfolios as a
diversier and a number of nancial rms are beginning to accommodate this demand.
It is thus appropriate as an academic pursuit to explore how the Bitcoin economy is
populated and extract the map of payment relationships, and to furthermore to trace the
evolution of those relationships over time to build up a better understanding of its political
economy. This paper takes a step in that direction by identifying the interconnectedness of
economic agents that use the Bitcoin payment network to transfer the digital currency
among each other internally (meaning within-Bitcoin transactions and not transactions to
exchange Bitcoin for other currency). To do this, we start by identifying and clustering
together the minimum units of Bitcoin identity, which are the individual addresses, into
what we call super clusters. We then we tag those clusters using a novel method to de-
anonymize economically relevant addresses and sort them into distinct categories. Finally,
we describe the dynamics of howthese clusters behave over time.
In this context, a super cluster can be thought of as an approximation of a discrete business
entity in that it describes a group of Bitcoin addresses that are owned or controlled collectively
for some particular economic purpose by the same party[3]. Although exact identities of such
super clusters can remain unknown, we are able to allocate many of them to specic business
categories as either an exchange, mining pool, online gambling site and black market or
composite of two or more of these categories by analyzing their specic transaction patterns
(TPs), as observed during the period 2009-2015. With this information, we unveil and study the
Evolution of
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95
Bitcoin network of payment relationships both among super clusters and also between super
clusters and their users: traders; gamblers; or black market user-dealers[4].
We are subsequently able to identify three distinct regimes that have existed in the
Bitcoin political economy, as it has grown and developed. First, a proof of conceptor
mining-dominatedphase, followed by a sinor gambling/black market-dominated
phase, and nally a maturationor exchange-dominatedphase. The novelty of our study,
moreover, is to elaborate and advance a general de-anonymization methodology that allows
us to link clusters composed of groups of addresses to identiable business categories,
which we use to map the systems evolution.
It is possible to accomplish such a map of activity and interaction among Bitcoin users
because pseudonimity, rather than strict anonymity, is a dening characteristic of the
Bitcoin network (Reid and Harrigan, 2013). As such, the true identities of users are hidden
behind their addresses that work as aliases, but which may be revealed upon transacting
with somebody else[5]. In other words, if Alice remits payment to Bob, then their identities
will be revealed to one another by virtue of exchanging addresses to send or receive bitcoin.
There are a few approaches suggested for revealing such identities in a systematic manner.
One proposed approach for de-anonymization is by mapping Bitcoin addresses to identiable IP
addresses. Kaminsky (2011) proposes that if we are able to connect to every node, the [IP of the]
rst node to inform you of a transaction of the source is it. Informed by this idea, Koshy et al.
(2014) conducted the rst trial using this method and managed to map nearly 1,000 Bitcoin
addresses to their ownersIPs. This method, however, is greatly limited when transactions are
executed through proxy services, which is not an inconsequential caveat. Another approach is to
cluster Bitcoin addresses into a single entity and then try to link this entity with a realname, as
described by Lischke and Fabian (2016); our work here continues along this second track.
There are two general procedures that must be clearly dened at the onset: clustering
and labeling. Clustering refers to grouping together all the addresses that belong to the
same benecial owner (i.e. a legal entity or individual person) into a unique assemblage.
This approach requires one to apply what we will call either the input address heuristic
and/or the change address heuristic, which are described in detail just below[6]. After
clustering, one can then apply labeling, whichconsists of either[7]:
manually tagging Bitcoin addresses to specic entities by directly participating in
Bitcoin transactions with those entities; or
scraping information from Web pages on the internet where, for any reason, the
identity of Bitcoin address holders is made public and can be extracted.
According to the input address heuristic, Bitcoin addresses used as inputs either
synchronously in the same multi-input transactions or asynchronously in different multi-
input transactions (when at least one input address is shared), are grouped together in
clusters. In other words, if address x and address y are both inputs to a unique transaction,
then we assume addresses x and y must also belong to the same cluster. Furthermore, if
both address y and address z belong to some other transaction, we would infer that
addresses x, y and z all belong to the same cluster. From the beginning of Bitcoin, Nakamoto
(2008) indirectly recognized the power of the input address method by saying that:
[s]ome linking is still unavoidable with multi-input transactions, which necessarily reveal that
their inputs were owned by the same owner. The risk is that if the owner of a [public] key is
revealed, linking could reveal other transactions that belonged to the same owner.
Later, Ron and Shamir (2013) extensively discuss the input address method and apply it via
the Union-Find graph algorithm in a study of the Bitcoin network through the May 13, 2012.
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Within the scope of this heuristic, other projects including Spagnuolo (2013) and Doll et al.
(2014) try to provide some practical applications of the theory by developing front-end Web
services to show, in real-time, identity correspondent to a specic address query. In
particular, Spagnuolo (2013) proposes the BitIodine tool, which parses the blockchain and
clusters addresses that are likely to belong to the same user or group of users, classies such
users and labels them.
With the change address heuristic, a cluster is composed of the input addresses plus the
output addresses that are predicted to be change addresses for a transaction. A rst
proposal approximating this heuristic comes from Androulaki et al. (2013), who naively
assume that [i]n the current Bitcoin implementation, users rarely issue transactions to two
different users. Presumably, this assumption perhaps once held in the past, but it is no
longer the case. Therefore, this initial version of the change address heuristic is relatively
fragile compared to the input address heuristic, and aggressive implementations require
large amounts of hand tuning to prevent false positives[8]. Meiklejohn et al. (2013)
reevaluate the change address heuristic and apply it cautiously by identifying only one-time
change addresses under the following conditions:
the transaction is not one that involves new coin generation;
it is the rstappearanceoftheaddressandatthesametimenottherst appearance for
all other output addresses (i.e. all the other addresses have been previously used); and
there is no other address in the output that is the same as the input address (i.e. no
self-change address).
The assumption behind this enhanced version of the change address heuristic is that the
change address is newly generated by the users wallet; even the owner may not
acknowledge its existence. In contrast, the receivers address is known in advance and
notied to the sender. Thus, also the one-time change address requires signicant human
adjustment to avoid excessive false positives when:
the receiver is a new user or creates a new address never used before;
the transaction output has two receiversaddresses without change address; and
the sender uses an old address to receive change, or there is no change transaction at all.
Despite some studies that rely on this version of the change address heuristic, e.g. Garcia
et al. (2014), for the purpose of our study, we opt for using a version of the input address
heuristic. Although our method is subject to some false negatives as it only considers
eligible for clustering addresses being used as transaction inputs, it is nonetheless robust to
false positives. This is crucial as any false positive would compromise the results of the
pattern analysis we apply later to ascribe the clusters to particular business categories; in
such a case, the clusters themselves would be composed of wrong addresses that would
likely follow incorrect behavioral patterns. This pitfall is avoided in our methodology.
False positives could have conceivably become a problem with the introduction, since 2013,
of the coinjoinpractice; refer Kristov Atlas (2015) and Tasca (2015). Coinjoin is an example of
a tool used to actively anonymize transactions within a distributed ledger. The principle behind
the method is quite simple: if for example, Alice wants to send one bitcoin to Bob, and Carla
wants to send one bitcoin to David, a coinjoin transaction could be established whereby the
addresses of Alice and Carla are both listed as inputs, and the addresses of Bob and David are
listed as outputs in one unique transaction. Thus, when inspecting the 2-to-2 transaction from
outside it is impossible to discern who is the sender and who the recipient. In this hypothetical
example, we would not be able to tell if it is Bob or David who is the recipient of Alice. If this
Evolution of
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97
sketch of the coinjoin principle were its actual implementation, then the input address method
could mistakenly cluster together the addresses of Alice and Carla as if they belonged to the
same entity. However, in practice, a coinjoin transaction works in a crucially different way:
the coinjoin technique shufes the addresses of users, but it also creates new batches of unique
addresses that are subsequently added to the usersaddresses and mixed together with these
transactions as well. The result is that coinjoin addresses could be reused several times along
with several other addresses from different users. Thus, novel unknown large clusters are
created that do not belong to any precise business category because their addresses are very
likely linked to more than two distinct entities or directly to coinjoin service providers. Those
clusters are similar to black holesas they absorbaddresses that should have been enclosed
within other clusters having a clear business prole and are not then misattributed to one of
our known categories. To sum up this explanation, even in the presence of coinjoin
transactions, our method is robust because the likelihood of encountering false positives is for
all intents and purposes negligible.
In this study, the result of applying the input address heuristic returns more than
30 million clusters, which reduces to a more manageable 2,850 when considering only those
composed of at least 100 addresses and that have received at least one thousand bitcoins
from January 2009 through May 2015[9]. We label such clusters super clustersbecause
they represent big agents with a strong presence and intensity of economic activity in the
Bitcoin system. All together, these super clusters transacted hundreds of billions of dollars
worth of notional value over the study period, at the current exchange rate.
We acknowledge that it is impractical to correctly identify all of the 2,850 individual
super clusters in the sample. However, our study has a less ambitious aim, which is to
ascribe all these super clusters to a given broader business category and explore their
network of business relationships, rather than drill down on individual identities. With that
in mind, from a list of publicly available pre-identied addresses obtained from the internet
we do successfully identify 209 super clusters out of the 2,850 as a seed for attributing
unknown clusters to business categories. In other words, this subset of known clusters,
which we call the known group, is used as the benchmark to identify the business
category of the remaining 2,641 clusters in the unknown group. Following from this, we
conclude our study by unveiling the network of payment relationships between these 2,850
super clusters, and by exploring the relative interdependence among business categories.
The paper proceeds as follows: Section 2 introduces some preliminary denitions;
Section 3 describes the data set we draw upon; Section 4 introduces pure user group (PUG)
analysis to classify super clusters in the unknown group using what we know of the known
group; Section 5 elaborates on the PUG analysis with a TP analysis, examining transaction
inows and outows among those super clusters in known group; Section 6 back-tests the
results from the PUG analysis; and Section 7 describes the network of entities on the Bitcoin
network as discerned from the PUG and TP analyses and develops the progression of three
distinct regimes that have existed over the course of the Bitcoin political economy.
2. Preliminary denitions
As explained in Section 1, the building block of our analysis is the concept of clustering
Bitcoin addresses. In this section, we provide a formal denition of clustering by omitting
unnecessary technical information which may turn out to be redundant and therefore not
useful for the scope of our analysis.
We dene the set Tx of all the Bitcoin transactions, occurred during the period of our
analysis, as Tx =(tx
1
,...,tx
i
,...,tx
z
). To each element tx
i
of Tx corresponds the cluster set
c
i
=(a
1
,a
2
,...a
n
)
i
containing all the input addresses (a
1
,a
2
,...) used in the transaction tx
i
.
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By using a variant of a Union-Find graph algorithm (?), if two or more clusters directly or
indirectly (via other clusters) have at least one address in common, we merge those clusters
into a single unique one. At the end of the merging process, we get C={c
1
,...,c
x
,...,c
y
,
...,c
z
} which is the set of all disjoint clusters such that c
x
\c
y
=1for all c
x
,c
y
[C. Let
W(C)beanite set W(C)={w
xy
(c
x
,c
y
)|c
x
,c
y
[C, c
x
=ƒc
y
}|{w
xx
(c
x
,c
x
)|Vc
x
[C}. Then,
W(W(C) is the set of all (direct) transaction (with loops[10]) between clusters, where w
xy
is
the total quantity of bitcoins transferred from cluster c
x
to cluster c
y
:
wxy ¼wxy if there is a transation from cxto cy:
0 otherwise:
We dene a super cluster, cˆ
x
, as any special cluster that belongs to the partition C
ˆ
C:
^
C¼cx2CX
z
h¼1
whx ch;cx
ðÞ
1;000 BTC ^nc
x
ðÞ
100
()
:(1)
where n(c
x
) denotes the number of addresses in cluster x.
According to our denition, a super cluster is any cluster that satises the following two
thresholds:
(1) having received at least 1,000 bitcoins during our research window; and
(2) comprising at least 100 unique addresses.
The rst threshold is necessary to increase the likelihood of excluding inactive entities from
the analysis. The second threshold is necessary to exclude as many private individuals as
possible, who typically own only one or a few addresses. Together, these thresholds increase
the robustness of the TP analysis of the clusters in Section 5, which is based on statistics
requiring big enough data. In fact, smaller clusters composed of some tens, or even some
hundreds of addresses are only able to generate a trivial amount of transaction data, giving
us insufcient information to perform a meaningful analysis.
3. Data set
In our study, we parsed data from the Bitcoin Core over the period of the January 3, 2009
(block 0) through May 8, 2015 (block 355551)[11]. Over this interval, the Bitcoin network
proliferates both in terms of number of addresses and in terms of number of transactions. Refer
Table I for a summary of our data set. All the data related to Bitcoin transactions are imported
into and managed via a MySQL database (see the diagram in Figure A1 in Appendix 1).
By applying the input address heuristic, 75,191,953 unique Bitcoin addresses are
grouped into 30,708,660 clusters, of which, about two-thirds are clusters composed of only a
single address, as shown in Table II.
Then, by applying the criteria dened in equation (1), 2,850 super clusters are ltered out.
Figure 1 shows the network of super clusters C
ˆ
and their transactions among each other, as
well as with all the remaining clusters in C\C
ˆ
.
By gathering publicly available address information, we are able to link part of super
clusters cˆ
x
[Ĉto real-world entities (e.g. BTCChina, Kraken, Xapo) which belong to different
business categories. Specically, we gathered 359,776 deciphered addresses from ? and ?.
According to their entity information, we could compose a group of deciphered sets of
addresses, P¼p1;p2; :::; pY; :::
fg
¼S
Y2C
pY. Precisely, p
g
={a|abelong to the known
Evolution of
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economy
99
benecial owner
g
} is the set of addresses that belong to the benecial owner
g
whose
identity is publicly available from the internet[12].
Thus, depending on whether a super cluster hold at least one address belonging to any p
g
[P
or not, C
ˆ
is then decomposed into either a known group, C
ˆK
, or a unknown group, C
ˆU
.Formally:
^
C¼^
CK[^
CU(2)
with C
ˆK
\C
ˆU
=1by denition and:
^
cx2
^
CKif ^
cx\p
g
1^^
cx\Pnp
g

¼1;8
g
2C
^
CUif ^
cx\p
g
1^^
cx\Pnp
g

1;8
g
2C
^
cx\p
g
¼1;8
g
2C:
8
>
<
>
:
(3)
The matching exercise turns out the following result: n(C
ˆK
)=209andn(C
ˆU
)=2,641suchthat
n(C
ˆK
)þn(C
ˆU
)=n(C
ˆ
) = 2,850[13]. As a side note, we remark that equation (3) follows a prudential
principle that aims to avoid false positives. Namely, any cluster in C
ˆ
that has addresses linked to
more than one set p
g
[P, is considered unknown and conned to the set C
ˆU
[14].
Then, according to their business model, each identied super cluster is allocated
into one of the following primary business categories: exchange C
ˆKX
, mining pool C
ˆKP
,
online gambling C
ˆKtt
,black market,C
ˆKB
. Besides these big four business categories
which are populated by economic entities with a clear business prole, there are also
few other economic entities with a business models (e.g. bitcoin wallets) heterogeneous
among them and disparate from the previous ones. Then, we classify them into the
category others,C
ˆKO
.
Table AI in the Appendix 2 shows us the results, namely, n(C
ˆKX
)=104,n(C
ˆKP
)= 18,
n(C
ˆKtt
) = 45, n(C
ˆKB
)= 13 and n(C
ˆKO
)= 29 such that n(C
ˆKX
)þn(C
ˆKP
)þn(C
ˆKtt
)þn(C
ˆKB
)þ
n(C
ˆKO
)=n(C
ˆK
)=209.
Figure 2 shows the payment network of the 209 identied super clusters[15]inC
ˆK
. In the
next sections, we will use the information on the super clusters in the set C
ˆK
together with
the information on their interactions with all the other clusters in C\C
ˆK
to derive the business
membership of each unknown super cluster in C
ˆU
.
4. Pure user group analysis
The PUG analysis is carried out to classify (into specic business categories) super clusters
in the unknown group, and it is based on the denition and classication of pureusers. By
Table I.
Blockchain database
facts
Bitcoin core parsed from the January 3, 2009 until May 8, 2015
Max block height 355,551
Total number of transactions 68,030,042
Total number of input 172,743,139
Total number of output 194,476,567
Total addresses identied 75,191,953
Total clusters identied(include at least one address) 30,708,660
Number of clusters with at least two addresses 9,847,999
Total transactions between clusters 88,950,021
Source: Bitcoin core
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pure users we mean all those clusters populating the Bitcoin economy (except for those
already in the known group) that had bilateral transactions with super clusters (in the
known group) belonging to only one business category. In other words, for each specic
business category, we build a correspondent PUG:
clusters having transactions only with exchanges in C
ˆKX
are classied in the PUG
traders;
clusters having transactions only with gambling services in C
ˆKtt
are classied in the
PUG gamblers; and
Figure 1.
Network
visualization of the
interactions of the
super clusters in C
ˆ
with each other and
also with all the
remaining clusters in
CC
ˆ
Evolution of
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101
clusters having transactions only with black market services in C
ˆKB
are classied in
the PUG black market user-dealers.
The classication of the clusters into different PUGs is the rst step of the PUG analysis.
The second step consists of classifying the super clusters in the unknown group into a
specic business category in the case they transact only with the corresponding specic
PUG. For example, those super clusters in the unknown group that had transactions only
with traders are classied as exchanges and so on also for the other categories. However, the
clusters in the known group identied in the categories mining pools and others follow a
peculiar business model. Thus, we do not create the set of PUGs having transactions only
with mining pools in C
ˆKP
because the mining pools in the unknown group will be identied
via the coinbase analysis (Section 4.2). Similarly, we do not create the set of PUGs having
transactions only with others in C
ˆKO
because those clusters do not have a clearly dened
business prole. In other terms, to avoid false positives, we will not try to classify super
clusters in the unknown group into the category others.
4.1 Pure user group identication
In this rst part of the analysis, we consider only the following sets C
ˆKX
,C
ˆKtt
and C
ˆKB
.
Accordingly, we introduce the following set notation: U
X
C\C
ˆK
is the subset of pure traders
that had transactions only with exchanges in C
ˆ
KX; U
tt
C\C
ˆK
is the subset of pure gamblers
that had transactions only with gambling sites in C
ˆKtt
;U
B
C\C
ˆK
is the subset of pure black
market user-dealers that had transactions only with black markets in C
ˆKB
.Formally:
UX¼cx2Cn^
CK9^
cy2^
CKX :wxy cx;^
cy

>0Úwy;x^
cy;cx

>0
(
X^wxj cx;cj
ðÞ
¼0Úwjx cj;cx
ðÞ
¼0X;8cj 2^
CKn^
CKX ):(4)
Utt ¼cx2Cn^
CK9^
cy2^
CKtt :wxy cx;^
cy

>0Úwy;x^
cy;cx

>0
(
X^wxj cx;cj
ðÞ
¼0Úwjx cj;cx
ðÞ
¼0X;8cj 2^
CKn^
CKtt):(5)
Table II.
The clusters
identied with the
input address
heuristic are grouped
per number of
addresses composing
them
Input address heuristic: clustering result
Number of addresses included in each cluster Number of clusters identified
>10001 194
100110000 1,145
101 1000 12,185
11100 436,093
210 9,398,382
=1 20,860,661
Total number of clusters 30,708,660
Source: Bitcoin Core
JRF
19,2
102
Figure 2.
Network
visualization of the
209 super clusters in
the set C
ˆ
K
that have
been identied by
cross-linking the
known addresses in
the set Pwith the
addresses in each cˆ
x
C
ˆ
Evolution of
the bitcoin
economy
103
UB¼cx2Cn^
CK9^
cy2^
CKB :wxy cx;^
cy

>0Úwy;x^
cy;cx

>0
(
X^wxj cx;cj
ðÞ
¼0Úwjx cj;cx
ðÞ
¼0X;8cj 2^
CKn^
CKB):(6)
This rst part of the PUG analysis returns the following results: n(U
X
) = 440, 434, n(U
tt
)=
415, 528 and n(U
B
) = 74, 233.
The statistics of the bitcoin transactions between pure users and clusters in the known
group reveal that the average volume per transaction differs substantially with respect to
each business category: The average volume per transaction from/to traders to/from
exchanges is 20 BTC; the average volume per transaction from/to gamblers to/from
gambling services is 0.5 BTC; and nally, the average volume per transaction from/to user-
dealers to/from black market services is 3 BTC (Table III).
4.2 Identication of mining pools
The super clusters in the category mining pool are identied without using the PUG
analysis. Indeed, each newly generated Bitcoin block includes a reward to the successful
miner: an amount equal to the sum of the block reward (or subsidy), i.e. newly available
bitcoins, plus any accumulated fees paid by transactions included in that block. To allocate
this sum, a new generation transaction is created whose input, called the coinbase,
contains the reward for the miners. Thus, unlike all other transaction inputs, the coinbase is
not linked to any previous output. This feature offers a simple and direct method to identify
those clusters belonging to the mining category by ltering out the transactions with null
input and only one output.
Let C
ˆUCoinbase
be the set of clusters (in the unknown group) composed (also, but not only) of
addresses with coinbase inputs, n(C
ˆUCoinbase
) = 575. Not all these 575 clusters, however, can
reliably be dened as mining pools; for some of them, mining is not their primary activity and
rewards from coinbase transactions represent only a small per cent of their activity. To make
sure that the taxonomy is robust, we classify only clusters in the unknown group whose
mining rewards occupy more than 80 per cent of its total income, as mining pool, C
ˆUP
.The
remaining clusters C
ˆUP
=C
ˆUCoinbase
\C
ˆUP
that cannot be dened as mining pools according to
our threshold are instead classied via the PUG analysis.
4.3 Classication of unknown super clusters
The principle of PUG classication for unknown clusters is straightforward and works
as follows: If one super cluster in the unknown group transacts only with one specic
Table III.
For PUG in different
categories, the table
summarizes the
average transaction
amount (BTC) and
average transaction
interval (minutes)
Statistics for PUG transaction
PUG !C
ˆ
K
C
ˆ
K
!PUG
PUG
Num of
clusters
Avg tx
volume (BTC)
Avg tx
interval (min)
Avg tx
volume (BTC)
Avg tx
interval (min)
UX
440,434 23.4 20,529.5 17.6 10,685.0
Utt
415,528 0.5 528.3 0.5 387.3
UB
74,233 2.7 22,151.3 3.4 9,394.0
JRF
19,2
104
PUG, then we suspect that this cluster belongs to the business category correspondent
to that specic PUG. For example, if during the period January 2009-May 2015, one
super cluster in C
ˆU
records transactions with one or more traders in U
X
but not with
gamblers in U
tt
and user-dealers in U
B
, it is classied as an exchange. One should note
that this does not rule out the possibility for the exchange to transact with any other
cluster in Cbeyond those in U
X
. The clusters who transact with multiple PUGs are
identied in the composite category, C
ˆUM
,which implies those super clusters might
have multi-business lines.
Let:
^
CUX ¼^
cx2^
CU9cy2UX:wyx cy;^
cx

>0^wxy ^
cx;cy

>0X
()
(7)
be a broad subset of exchanges in C
ˆU
that have transactions not only with traders.
Let:
^
CUt ¼^
cx2^
CU9cy2Utt :wyx cy;^
cx

>0^wxy ^
cx;cy

>0X
()
(8)
be a broad subset of gambling services in C
ˆU
that have transactions not only with
gamblers.
Let:
^
CUB ¼^
cx2^
CU9cy2UB:wyx cy;^
cx

>0^wxy ^
cx;cy

>0X
()
(9)
be a broad subset of black market services in C
ˆU
that have transactions not only with user-
dealers.
Then, the subset of exchanges in C
ˆU
that have transactions only with traders in
U
X
is:
^
CUX ¼^
cx2^
CUX n^
CUt [^
CUB [^
CUP

:(10)
Similarly, the subset of gambling services in C
ˆU
that have transactions only with gamblers
in U
tt
is:
^
CUtt ¼^
cx2^
CU
tn^
CUX [^
CUB [^
CUP

:(11)
The subset of black market services in C
ˆU
that have transactions only with user-dealers in
U
B
is:
^
CUB ¼^
cx2^
CUB n^
CUX [^
CUt [^
CUP

:(12)
Finally, the subset of multi-business clusters in C
ˆU
that have transactions with more than
one user group is:
Evolution of
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105
^
CUM ¼(^
cx2^
CUX [^
CUB [^
CUt [^
CUP

n^
CUX [^
CUtt [^
CUB [^
CUP

X):
(13)
Table IV shows that n(C
ˆUX
)=310,C
ˆUtt
=755,C
ˆUB
=41,C
ˆUP
= 57 and C
ˆUM
=630.
5. Transaction pattern analysis
In this section, we introduce a TP analysis to study the different TPs of the super clusters in
set C
ˆK
(listed in Table AI). The TP analysis is used to garner more insights into stylized
facts characterizing the distinct business behaviors of the super clusters. Moreover, the TP
analysis is used in Section 6 to measure the accuracy of the PUG analysis by testing the
pattern similarity between the clusters in C
ˆK
and those in C
ˆU
. In the following, we divide the
TP analysis in inow and outow analysis.
5.1 Inow analysis
The inow analysis consists of examining the properties of the transactions toward any
super clusters in the known group C
ˆK;
. We select the transactions in the set:
W
!KW¼wyx cy;^
cx

2Wcy2Cn^
CK
;^
cx2^
CK
;nyx 100
()
(14)
where n
yx
denotes the number of transactions from cluster c
y
to cluster cˆ
x
during the
period of the analysis. According to equation (14),apair(c
y
,cˆ
x
)[W
!Kis only
considered if there has been at least 100 transactions from c
y
to cˆ
x
.Thisminimum
transaction threshold is subjective and shall be set to any value able to ensure that the
descriptive statistics calculated are robust. In our case, with n
yx
100, we obtain that n
(W
!K)=11, 899, involving 148 super clusters in x^
CKx. After having dened the set of
analysis, we calculate the median of transaction volume and the median of time interval
in minutes for each pair (c
y
,cˆ
x
)[W
!K[16]. Each dot in Figure 3 represents the
measurement for one pair (c
y
,cˆ
x
)[W
!K
:redifcˆ
x
[C
ˆKX
,green if cˆ
x
[^
CKP,blue if cˆ
x
[C
ˆKtt
and black if cˆ
x
[^
CKB .Thex-axis is the median of the transaction volume and the y-axis
is the median of the time interval (in minutes) between inow transactions for each pair
(c
y
,cˆ
x
)[W
!K.
Table IV.
The number of
clusters tagged with
the PUG method
Tagged cluster in unknown group
Category No. of clusters
n(C
ˆ
UX) 310
n(C
ˆ
Utt) 755
n(C
ˆ
UP) 57
n(C
ˆ
UB) 41
n(C
ˆ
UM) 630
Notes: To give the reader a complete view, C
ˆ
UP
is also listed here, which is identied from coin base
transactions
JRF
19,2
106
Figure 3 shows some clustering effects; we can see that for each of our four identied
business categories there exists specic patterns of transaction behavior. For example, there
are clearly plotted in blue, vertical lines at x= 0.01, 0.02 and so on. To capture this more
clearly, we plot the kernel density in Figure 4[17]. This illustrates a notable characteristic for
gambling behavior, that is gamblers tend to place bets with similar, round lot amounts
Figure 3.
Inow TP for the
known group
104
103
Inflow Transaction Pattern (Known Group)
Exchange
Miner
Gambling
Black market
102
101
100
10−4 10−3 10−2 10−1 100101102103
Median of Received Amount (BTC)
Median of Tx Interval (minute)
Notes: Each dot characterizes one pair of clusters (cy, cx) W K. The
x-axis is the median transaction volume of all transactions between all the
pairs of clusters WK during the period January 2009-May 2015. The
y-axis is the median transaction interval (in minutes) of the transactions
between all the pairs of clusters WK
ַַ
Figure 4.
Kernel density of the
inow amount of
bitcoins received by
any c
y
[C\C
ˆKduring
the period January
2009-May 2015 by
each cluster cˆ
x
[C
ˆkin
the known group
Kerneldensity
Kerneldensity
x10
5
8
Exchange (Known Group)
x10
6
3
Mining Pools (Known Group)
2.5
6
2
41.5
1
2
0.5
0
0.001 0.01 0.02 0.05 0.1 0.20.3 0.5 1
Received Amount
0
0.001 0.01 0.02 0.05 0.1 0.20.3 0.5 1
Received Amount
x10
6
8
Online Gambling (Known Group)
x105
8
Black Market (Known Group)
66
44
22
0
0.001 0.01 0.02 0.05 0.1 0.20.3 0.5 1
Received Amount
0
0.001 0.01 0.02 0.05 0.1 0.20.30.5 1
Received Amount
Kerneldensity Kerneldensity
Evolution of
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economy
107
(i.e. 0.1, 0.5, 1.0) again and again, with wagers of 0.01 BTC being placed most frequently.
Gamblers may be accustomed to wagering in round amounts in traditional settings using
casino or poker chips with specied round values (e.g. $1, $5 or $25), or online using virtual
chips. Individuals may carry forward that behavior to bitcoin-based gambling even in
instances where the size of bets is determined arbitrarily by the gambler placing bets[18].
With respect to exchanges, although it is less obvious due to some overlap in the plot, we
are still able to see some vertical lines in red at x= 0.1, 0.5 and 1.0, which indicates that the
traders usually deposit into exchanges round amounts of bitcoins, rather than random
amounts to presumably exchange them for at or alternative digital currency. In other
words, it appears that traders may wait until they have accumulated some even amount,
most commonly 1.0 BTC, before selling them.
Inows to black markets show a wider variety of arbitrary transaction size, but still also
show marked preference for round lots of bitcoin, notably at amounts of 0.1, 0.2, 0.3, 0.5 and
1.0 BTC. This may suggest that black market sellers explicitly place round lot prices on
their items as a matter of doing business. Prescription and illegal drugs are notably sold on
black markets, and this indicates that sellers will offer an amount of contraband that
corresponds to a round price (say 1.0 BTC), rather than determining what theprice would be
for a xed quantity (say for 1 ounce)[19].
Mining pools exhibit a more or less random pattern of inows, as a mining pool will only
be credited with small amounts of bitcoin whenever it nds a new block of bitcoin. When
this happens, the pool will generally extract a small prot consisting of either a nominal
percentage of the block reward or of the transaction fees associated with that block, or both.
In addition to studying patterns in the amounts of bitcoin inows, we also consider
transaction intervals. We observe a large density of dots, plotted in red, clustering horizontally
just above y=1,000inFigure 3,specically at 1,440 min, which is the number of minutes in one
day. What this shows us is that there are a large number of traders who send small amounts of
bitcoins to exchanges regularly each day. We suspect that these could be small miners who
exchange mined bitcoins for cash on a daily basis, or day traderswho are active daily but go
home at, having sold out any positions in bitcoin to avoid overnight price volatility. Figure 5
claries this effect and shows the kernel density of the intervals between transactions (band =
100 min). The 1,440-min interval is prominent not only for traders to exchanges but also for the
other business categories, suggesting that a one-dayholding period for bitcoin transactions is
somewhat typical; a one-day effect where traders, gamblers, black market participants and
miners tend to cash out on a daily basis.
Figure 5.
Kernel density of the
inow transaction
intervals between
subsequent
transactions during
the period January
2009-May 2015 for
each category in the
known group
JRF
19,2
108
We observe however that gambling has, by far, the shortest interval as well as the highest
transaction frequency. This is not difcult to understand, as gamblers can ante or re-bet
many times in a matter of minutes.
5.2 Outow analysis
The outow analysis consists of examining the properties of the transactions from the
clusters in the known group C
ˆK
. As for the inow analysis we measure the median of
transaction volume and the median of time interval in minutes. Additionally, we
measure the median number of inputs and outputs in the transactions between each pair of
clusters. To examine the transaction outow from the super clusters cˆ
x
[C
ˆK
, we select the
transactions in the set:
W
K
W¼wxy ^
cx;cy

2W
^
cx2^
CK
;cy2Cn^
CK
;nxy 100
()
(15)
where n
xy
denotes the number of transactions from cluster cˆ
x
to cluster c
y
during the period
of the analysis. According to equation (15), a pair (cˆ
x
,c
y
)[W
K
is considered only if there
has been at least 100 transactions from cˆ
x
to c
y
. From our database, we obtain that n(W
K
)=
16,188, involving 148 super clusters in C
ˆK
. To extract information about the number of
inputs/outputs of the transactions in W
K
we plot the median number of inputs/outputs in
all the transactions between each pair (cˆ
x
,c
y
)[W
K
. Each dot in in Figure 6 represents a
value set for pair (cˆ
x
,c
y
,) [W
K
: red if cˆ
x
[^
CKX ,green if cˆ
x
[C
ˆKP
,blue if cˆ
x
[C
ˆKtt
, and black if
cˆ
x
[^
CKX . The x-axis represents the median number of inputs, and the y-axis represents the
median number of outputs.
Figure 6.
Median number of
inputs and outputs
for all the
transactions among
each pair (cˆ
x
,c
y
)
/
W
K
during the period
January 2009-May
2015
Evolution of
the bitcoin
economy
109
Only the mining pools show a signicantly distinct TP from the others. Specically, the
outow transactions for most of the mining pools are characterized by no more than ten
inputs, but at the same time by a large amount of outputs, ranging from tens to thousands.
This is consistent with the business model of mining pools: After successfully mining
bitcoins, the mining pools will distribute the reward to all the small miners who have
contributed some mining effort. So, the number of outputs is much larger than the number of
inputs. One could speculate on the size of these mining pools according to the number of
outputs in each outow transaction.
As done for the inow analysis, for each pair (cˆ
x
,c
y
,) [
/
W
K
,we calculate the median
transaction volume and the median time interval in minutes. The x-axis in Figure 7
represents the median transaction volume for each pair (cˆ
x
,c
y
,) [W
K
while the y-axis
represents the median time interval (in minutes) between outow transactions for each pair
(cˆ
x
,c
y
,) [W
K
. The blue dots scattered around the bottom-left area of the plot imply that
gambling clusters send relatively small amounts of bitcoin but at a high-frequency to their
counterparts[20].
The outow transaction interval is plotted in Figure 8, which also shows the one-day
effect for mining pools. The combination of the results from Figures 5 and 8reveal a clear
stylized fact characterized by many small miners receiving daily rewards from mining pools
and then exchanging those rewards for at currency on exchange platforms.
6. Pure user group control test with transaction pattern analysis
In this section, we test the results of the PUG classication conducted in Section 4 for
clusters in the unknown group by analyzing whether they exhibit pattern similarities with
the clusters in the known group. The test is twofold and is based on the translation in matrix
Figure 7.
Outow TP for the
known group
JRF
19,2
110
form of the inow TPs and the outow TPs involving super clusters in the known group.
Each matrix is then compared, via a 2D correlation[21] analysis, with the correspondent one
related to the inow and outow TPs involving super clusters in the unknown group.
To start, we translate the patterns depicted in Figure 3 into a matrix of transaction volumes
and time intervals for all cluster pairs (c
y
,cˆ
x
)[
W
!
Kwith cˆ
x
[C
ˆ
K.Wethencreateamatrixof
transaction volumes and time intervals for all cluster pairs (c
y
,cˆ
x
)[
W
!
Uwith cˆ
x
[C
ˆ
Uwhere:
W
!UW¼wyx cy;^
cx

2Wcy2Cn^
CU
;^
cx2^
CU
;nyx 100
()
(16)
which means that a pair (c
y
,cˆ
x
)[W
!Uis considered only if there has been at least 100
transactions from c
y
to cˆ
x
.Foreachpair(c
y
,cˆ
x
)[W
!Ktt W
!KW
!KB W
!KW
!Kbe the
subsets of inow transactions towards exchanges, gambling, and black markets in the known
group, respectively. Similarly, let W
!UX
W
!U,W
!Ktt W
!KU W
!UB W
!Ube the
subsets of inow transactions toward exchanges, gambling and black markets in the unknown
group, respectively.
Then, the 2D correlations of the inow TPs between super clusters (in the known and
unknown group) and clusters outside the groups are dened as follows: corr2D(W
!KX,
W
!UX
)is
the 2D correlation between the inow TPs for the exchanges in the known and unknown
groups; corr2D(W
!Ktt ,W
!Utt
) is the 2D correlation between the inow TPs for the gamblers in
the known and unknown groups; corr2D(W
!KB,W
!UB
) is the 2D correlation between the
inow TPs for the black markets in the known and unknown groups.
The correlation matrix in Table V shows that the classication of the super
clusters according to the PUG analysis is consistent with the results of the TP
analysis because the correlations along the main diagonal are greater than the values
off-diagonal:
Figure 8.
Kernel density of the
outow amount of
bitcoins sent to any
c
y
[C\C
ˆKduring the
period January 2009-
May 2015 by each
cluster cˆ
x
[C
ˆkin the
known group
Evolution of
the bitcoin
economy
111
corr2DW
!KX
;W
!UX

>corr2DW
!KX
;W
!Utt

;
>corr2DW
!KX
;W
!UB

and
corr2DW
!Ktt
;W
!Utt

>corr2DW
!Ktt
;W
!UX

;
>corr2DW
!Ktt
;W
!UB

and
corr2DW
!KB
;W
!UB

>corr2DW
!KB
;W
!UX

;
>corr2DW
!KB
;W
!Utt

Finally, by following a reverse approach than the one adopted to build the 2D correlation
matrix for the inow TPs, we calculate also the 2D correlation between pairs of outow
transactions involving clusters in the known and unknown group. Table VI shows that also
in this case, the classication of the super clusters according to the PUG analysis is
consistent with the results of the TP analysis because the correlations along the main
diagonal are greater than the values off-diagonal.
7. The bitcoin network
From the PUG analysis, we are able to classify some unknown super clusters into specic
business categories. To illustrate the result, Figure 9 plots the payment network between the
super clusters in C
ˆ
and their counterparts. For the sake of visualisation pourpose, two
Table VI.
Correlation of
category transaction
(outow) pattern
between the known
group and unknown
group
Correlation matrix outflow transaction volume/interval matrix
W
!
UX
W
!
Utt
W
!
UB
/
W
UX
0.4984 0.0864 0.3599
/
W
Utt
0.0378 0.5933 0.0705
/
W
UB
0.4260 0.0632 0.4509
Table V.
Correlation of
category transaction
(inow) pattern
between the known
group and unknown
group
Correlation matrix inflow transaction volume/interval matrix
W
!
UX
W
!
Utt
W
!
UB
W
!KX
0.8183 0.2240 0.5090
W
!Ktt
0.0412 0.8943 0.0100
W
!KB
0.6615 0.0389 0.6665
JRF
19,2
112
thresholds are set for plotting: First, we only plot for transactions (edges) with a volume
larger than 1,000 BTC; second, the degree of the nodes must be larger than 2.
Figure 10 is a matrix of transactions between those super clusters in C
ˆ
ascribed to the
major business categories (exchange, mining pool, online gambling, black market and
composite). The y-axis depicts the sending clusters (grouped by business category) and the
x-axis depicts the receiving clusters (also grouped by business category). There is no
transaction volume limit for plotting this matrix; a dot is plotted as long as a y-axis super
cluster has ever sent (even once) bitcoins to an x-axis super cluster, no matter what the
transaction volume is. All the dots are colored according to the category to which the source
belong to. For example, all the transactions sent from exchanges are signied by red dots.
We observe that mining pools typically only send coins to other categories, and do not
receive any. We also observe that black markets tend to interact most with exchanges and
composite services. A more comprehensive analysis of the results shown in Figure 10 is
offered by the inow dependency matrix in Table VII.Table VII(A) lists the bilateral
transaction volume between all the pairs of business categories. The number in the cell (i, j)
is the amount category isent to j. For example, cell(6,1) = 6,003,342.66 tells us the category
traders sent around six million bitcoins to exchanges. Table VII(B) calculates, for a given
category, the percentage of bitcoins received from other categories. For example, cell(6,1) =
26.72 per cent shows us that the 26.72 per cent of the total inow for the category exchanges
comes from traders.
Figure 9.
Payment network
between super
clusters in and their
counter parts
Evolution of
the bitcoin
economy
113
7.1 Evolution of the bitcoin economy
Using the above analysis, we can measure the relative prevalence of each general business
category (i.e. mining pool, exchange, online gambling and black market) in our sample, and
track their evolution over the study period and visualize shifts in their relative centrality.
Figure 11 shows the income inow from January 2009 through May 2015.
We identify three distinguishable regimes that have occurred in the Bitcoin economy
since its inception. The rst period runs from approximately January 2009 through March
2012. This proof-of-conceptperiod is characterized largely by test transactions among a
small number of users, and with very little meaningful commercial activity[22]. Our analysis
shows that this initial period is dominated almost entirely by mining, which is what we
would expect from a system still devoid of material economic activity.
Next, from approximately April 2012 through October 2013 a second period consisting of
early adoptersappears. This period is characterized by a sudden inux of gambling
services and darknetblack markets; due to the overwhelming prevalence of these
arguably nefarious categories, another name for this phase could be the period of sin[23].
These types of businesses initially responded to the unique features of Bitcoin such as its
relative anonymity (pseudonimty), lack of regulatory and legal oversight, borderless
transactions and low transaction costs absent from taxation. This new form of secure digital
cash was ideal for the purchase and sale of illicit drugs, stolen items and other contraband
Figure 10.
Transaction matrix
between super
clusters in different
business groups:
EXfor exchange,
MPfor mining Pool,
OGfor online
gambling, BMfor
black market, CO
for composite
JRF
19,2
114
Inflow dependency matrix
Exchange Mining pool Gambling Black market (BM) Composite Trader Gambler BM user
A. Inow transaction matrix (in volume)
Exchange 12723006.59 5496.23 103072.23 377087.03 3026163.73 7311315.30 658.27 320.85
Mining pool 207146.47 152704.12 18717.09 1050.73 371430.37 1974.96 120.87 93.94
Gambling 66925.21 4440.94 20670014.00 14443.62 668594.19 5632.81 1493367.85 322.65
Black Market(BM) 384240.90 1573.13 30706.69 605954.69 534554.26 458.29 0.00 527171.73
Composite 3079138.63 19936.42 900872.93 858455.30 54573371.03 519874.09 145867.51 57825.19
Trader 6003342.66 2.61 1881.18 279.75 534557.53 0.00 0.00 0.00
Gambler 686.01 0.00 1296775.33 1093.50 210665.85 0.00 0.00 0.00
BM user 1492.33 0.00 2220.96 319168.90 76486.66 0.00 0.00 0.00
B. Inow transaction matrix (in percentage)
Exchange 56.63 2.98 0.44 17.31 5.04 93.26 0.04 0.05
Mining pool 0.92 82.92 0.08 0.05 0.61 0.03 0.01 0.02
Gambling 0.29 2.41 89.77 0.66 1.11 0.07 91.05 0.06
Black market(BM) 1.71 0.85 0.13 27.82 0.89 0.01 0.00 90.00
Composite 13.70 10.82 3.91 39.42 90.96 6.63 8.89 9.87
Trader 26.72 0.00 0.01 0.01 0.89 0.00 0.00 0.00
Gambler 0.00 0.00 5.63 0.05 0.35 0.00 0.00 0.00
BM user 0.01 0.00 0.01 14.65 0.12 0.00 0.00 0.00
Notes: The transaction ow in subtable(A) is from row to column. For example, cell(2,1) means mining pools send 207,146 BTC to exchanges. In subtable(B), the
percentage is calculated column-wise, such that the gure reects the inow ratio for each category. For example, cell(2,1) = 0.92 tells us 0.92% of total income for
exchanges is from mining pools
Table VII.
Transaction
relationship between
categories
Evolution of
the bitcoin
economy
115
that could not be easily traded elsewhere online, or for gambling from a location where such
a practice would be prohibited. Often, users of these sinsites would mask their internet
trafc via services such as a virtual private network or via the TOR network, encouraging
usage growth where the probability of being caught would be minimal (?). In fact, our data
show that in the January of 2013, gambling and black markets together accounted for fully
51 per cent of all transactional inows on the Bitcoin blockchain (in our sample).
Figure 12 shows the relative percentage of inow transactions for each business category
from January 2009 through May 2015.
The largest black market at the time was the Silk Road (Figure 9). That service was
famously raided and shut down by the FBI in October of 2013, which could help explain the
sudden drop in black market activity that brought this period to a close, although this event
cannot satisfactorily explain the concurrent drop in gambling activity. The drop in
gambling as a percentage of overall bitcoin transactions may have been due to the increase
Figure 11.
Stacked plot of the
inow income
amount for each
business category in
our sample (i.e. sum
of the bitcoin inows
across all the super
clusters in C
ˆ
belonging to the same
major business
category) over the
bitcoin network,
monthly from
January 2009 through
May 2015
x 10
6
Bitcoin Income byCategory
3.5
3
2.5
2
1.5
1
0.5
0
01/09 01/10 01/11 01/12 01/13 01/14 01/15
Black Market
Online Gambling
Mining Pool
Exchange
Figure 12.
Stacked plot of the
relative income for
each business
categories as a
percentage of total
income inows,
monthly from
January 2009 through
May 2015
Bitcoin Income Percentage by Category
100%
80%
60%
40%
20%
01/09 01/10 01/11 01/12 01/13 01/14 01/15
Black Market
Online Gambling
Mining Pool
Exchange
Notes: Mining dominates initially, then “sin” categories (gambling
in blue and black markets in black) rise, but recede over time in
favor of exchanges
JRF
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in value of one bitcoin, from a few tens of dollars to a few hundreds of dollars during this
time. If a gambler tends to bet nominally $100 per day, what used to translate into some
dozens of bitcoins instead became fractions of one bitcoin. Indeed, even though the relative
amount of gambling has declined, the absolute amount wagered in dollar terms increased
modestly over the study period. Still today, over 100 active gambling services currently
exist that use bitcoin. It is also worth noting that while the overall amount of business being
transacted on sinentities has fallen quite signicantly, the actual number of black market
sites available on the Bitcoin economy has nonetheless grown, with at least four reboots of
the Silk Road, and no less than fty other (now defunct) marketplaces established since
January 2014. There were still a dozen or more such darknet marketplaces active around the
time the study period ended in mid-2015 (Branwen, 2015).
Still, by November 2013, the amount of inows attributable to sinentities had shrunk
signicantly to just 3 per cent or less of total transactions. This third period, which we are
still currently experiencing, is characterized by a maturation of the Bitcoin economy away
from sinenterprise and diversifying into legitimate payments, commerce and services.
This claim is moreover supported by the ascendancy of the centrality of exchanges in the
Bitcoin network. Figure 13 takes the sum of the monthly betweenness centralities of the
super clusters in each business category, and it ranks them from January 2012 through May
2015[24]. Each cell is colored according to the category we have identied.
Since January 2014, we see red cells outnumber all the others in each column, which tells
us that exchanges are the center of transaction activity.
When a licit merchant or service provider enters the Bitcoin economy and accepts bitcoin
as payment, we expect that they will cash out on a steady basis in order to cover business
costs and to reduce exposure to bitcoins price volatility; in doing so, they require the regular
use of exchanges. At the same time, investors and other users who see bitcoin as a nancial
asset would increasingly require exchanges. It is also around this time that external venture
capital investment grew in support of Bitcoin-related start-ups and infrastructure, signaling
further legitimizing. According to startups in the Bitcoin space raised almost $1bn in three
years (Q1 2012-Q1 2015). In 2012, around $2m of VC money made its way to Bitcoin start-
ups. In 2013 that number had grown to $95m, followed by $361.5m in 2014 and more than
half a billion dollars in 2015.
Mining pools have stayed out of the spotlight in terms of our analysis of inows and
outows due to the cap on how many bitcoins are created each day. This should not
understate the signicance of miners and their role in the Bitcoin economy. First, we would
Figure 13.
Monthly evolution,
from January 2012
through May 2015, of
the sum of the
(betweenness)
centrality measures
across all the super
clusters in Cˆ
belonging to the same
major business
category in the
bitcoin network
Evolution of
the bitcoin
economy
117
not expect mining pools to receive much in the way of income as those who join pools will
only extract bitcoins away from pools and not send any to them. The pool generally earns
income by taking a nominal percentage (1-2 per cent or less) of the block reward and/or by
taking in the transaction fees associated with a found block. In terms of outows, despite the
amount of miners active on the network, the rate of unit formation for new bitcoins remains
xed at one block every ten minutes. At the beginning of our study period, the block reward
was 50 BTC per block, from March 1, 2009 until November 28, 2012, so on any given day
miners collectively produced just 7,200 BTC, a small fraction of total daily transaction
volume. After November 28, 2012 and until approximately July 9, 2016, the block reward
was reduced to 25 BTC, so that only 3,600 BTC were produced by miners daily, on average.
From the July of 2016, the block reward is again reduced by half to 12.5 BTC, or just 1,800
BTC to be produced per day in aggregate through mining. Therefore, even if all participants
of mining pools cashed out daily, their contribution to the overall network of payments will
always be trivial, and, in fact, will decrease over time as the block reward continues to
diminish. At the same time, however, the mining system serves a crucial function as it is the
de facto central bankof the Bitcoin economy, expanding the money supply and validating
every transaction. Without a robust andhonestsegment of miners, the delity of all other
payments in the network would be suspect. In fact, a weak network of miners would leave
the Bitcoin economy prone to a so-called 51 per cent attack, where a bad actor could begin to
censor transactions by controlling a majority of power that validates transactions. Even
though the relative centrality of miners is very small compared to the other business
categories, the value they confer on to the network may instead be manifest via the price of
bitcoin and minersmarginal protability (Hayes, 2016).
7.2 Limitations and future directions
This study is not without some important limitations. First, it is obvious that the price of a
bitcoin has risen wildly over the course of the study period and afterward, meaning that
some of our stylized facts observed between economic actors may no longer apply. For
example, the price of a $1,000 quantity of illegal contraband on a black market once
corresponded to 50 BTC now commands, say, just 0.25 BTC. While this is certainly likely to
diminish the absolute bitcoin volume of many transactions, price uctuations ought to have
no impact on the relative prevalence or centrality of category participants within the Bitcoin
economy itself. Indeed, it is the relative predominance of economic activity that we are most
concerned with and the pattern of transactions ows between actors. One important note on
this, however, that could become an issue for future work is our use of a 1,000 BTC
minimum activity threshold for identifying super clusters. As the price of a bitcoin rises
further, the number of new entities receiving such large amounts is likely to diminish;
therefore, this identication criterion probably ought to be relaxed.
Another concern could be the potentially biased use of coinjoin services by sin actors.
One may reasonably argue that only sinnerswould need to use anonymization tools on
their transactions, while those with nothing to hide would refrain from paying this added
cost (albeit small). As we describe earlier in the paper, the presence of coinjoin mixing does
not present the opportunity for false positives using our method; however, if such a bias for
coin mixing exists we may be confronted with false negatives excluded from the analysis. If
the use of coinjoin increased sharply at the transition to the maturation period, then this is a
plausible alternative explanation. A recent in-depth analysis of coinjoin transactions
conducted by Möser and Böhme (2017) show that the number of potential coinjoin
transactions grew from zero per block prior to late 2013 to relatively stable values between
10 and 15 transactions per block in 2014 and through 2015. During the same period, the
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number of total transactions per block grew from around 250 transactions to more than 500
in each block (Blockchain.info, 2017). This suggests the prevalence of coinjoin transactions
was at most around 6 per cent of total transactions at the onset of the 2013transition into the
period of maturity and as a percentage of total transactions has fallen substantially since.
A third limitation tothis preliminary study is that we do not provide condence intervals
or measure of statistical signicance for our correlation matrices, upon which our PUG
analysis relies. Our aim herein is meant principally to be descriptive in nature in order to
reveal the network of economic clusters and their transaction partners. As we only use four
major categories of economic activity (plus a fth grouping of unknowns), it would be
difcult to confuse, for example, a mining pool with a gambling service. However, future
work that aims to clarify and complement these economic sectors will no doubt benet from
including robustness checks in terms of relating the statistical signicance of correlation
coefcients.
Finally, our analysis makes no attempt at stating causality for the transitions between
one period to the next and makes no assertion that the period of maturity is destined to
continue uninterrupted. While it is unlikely that the Bitcoin economy will revert to a
renewed period of sin activity, it is possible that its legitimacy may be undermined for a host
of other reasons, foreseen or otherwise. A theoretical foundation drawing on the elds of
political economy, history of economics, and economic sociology may be able to provide
some insights into these questions of causal effect and trajectory.
As cryptocurrency networks continue to grow in scale and scope, and as they become a
more legitimate economic institution, analyses similar to this one ought to be carried out on
other blockchains, for example Ethereum or Litecoin. Doing so can lay the groundwork for a
comparative political economy of blockchain-based platforms.
8. Conclusions
As the Bitcoin economy grows in size and scope, it becomes increasingly important to better
understand the key components and players in that system. However, this task has largely
proven cumbersome as many tens of millions of individual addresses exist, which are not
obviously linked to any specic individual or business entity and simply represent
nondescript public keys in a public-private key pair.
In this paper, we begin to unveil the composition and trajectory of the Bitcoin political
economy by analyzing a database composed of millions of individual Bitcoin addresses that
we rene down to 2,850 super clusters, each comprised more than 100 addresses and having
received at least 1,000 BTC from January 2009 to May 2015. A super cluster is described as
an approximation of a business entity in that it describes a number of individual bitcoin
addresses that are owned or controlled collectively by the same benecial owner for some
particular economic purpose. These important clusters are, for the most part, initially
unknown and uncategorized. However, we can ascribe most of them to one of four specic
business categories mining pools, exchanges, online gambling sites and black markets
by mapping and analyzing the network of actors and pattern of payments among those and
a smaller known set of clusters. In particular, we achieve this mapping using a PUG analysis
that examines inows and outows to and from each cluster, as well as TP analysis to
conrm those ndings. Our method of de-anonymizing otherwise pseudonymous clusters
allows us to not only visualize the Bitcoin network of payments but also to extract stylized
facts that describe its internal economy.
We nd that there are, in fact, distinct patterns of transaction ows for actors in
each business category. For example, ows between traders and exchanges averaged
just around 20 BTC over the study period, and traders bought or sold on average every
Evolution of
the bitcoin
economy
119
11 days. Meanwhile, gamblers wagered just 0.5 BTC on average, but re-bet often within
the same day. There seems to be a strong preference to do business within the Bitcoin
economy in round lot amounts (e.g. 0.1, 0.2, 0.5, 1.0 BTC), whether it is traders
exchanging for at money, gamblers placing bets or black market goods being bought
and sold.
In terms of transaction interval, there is an observable one-day effect for each business
category during the study period. For instance, a pattern emerges that many (pooled) miners
accumulate mining rewards and subsequently sell those bitcoins on exchanges on a daily
basis. This is interesting, as it could suggest most miners are operating to sell their product
each day for-prot and are not mining to accumulate and hoard bitcoins for the long term.
Whether this observation has any bearing on the price of bitcoin is open to further study.
Transaction ows from miners in our sample, however, are a relatively small fraction of
total volume compared to the rest of the Bitcoin economy, as miners in aggregate were only
able to produce no more than either 3,600 (or 7,200 BTC) per day on average over the study
period with a block reward of either 25 (or 50 BTC prior to the block reward halving) due to
the limitation of the Bitcoin protocol that enforces a controlled rate of new unit formation at
one block every 10 mins.
Notes
1. By convention, we use Bitcoin with a capital Bto denote the protocol, network, and community,
while bitcoin with a small bdenotes the digital currency and units of that currency.
2. Refer Blockchain.info (2017).
3. In principle, a single entity may have control over more than one distinct super cluster if the
common ownership of some of their addresses is not evident from the data.
4. As well as other unknown individuals
5. A Bitcoin address is an identier of 26-35 alphanumeric characters that is derived from the public
key through the use of one-way cryptographic hashing. The algorithms used to make a Bitcoin
address from a public key are the Secure Hash Algorithm (SHA) and the RACE Integrity
Primitives Evaluation Message Digest (RIPEMD), specically SHA256 and RIPEMD160, refer
Antonopoulos (2014).
6. In the Bitcoin network, the output of a transaction is used as the input of another transaction. If
the input is larger than the new transaction output the client generates a new Bitcoin address,
and sends the dierence back to this address. This is known as change. From the Bitcoin wiki:
Take the case of the individual transaction 0a1c0b1ec0ac55a45b1555202daf2e08419648096
f5bcc4267898d420def87, where a previously unspent output of 10.89 BTC was spent by the
client. 10 BTC was the actual payment amount, and 0.89 BTC was the amount of change
returned. The client cannot spend just 10.00 BTC out of a 10.89 BTC payment anymore than a
person can spend 1 out of a 20 bill. The entire 10.89 BTC unspent output became the input of this
new transaction and in the process produced are two new unspent outputs which have a
combined value of 10.89 BTC. The 10.89 BTC is now spentand eectively destroyed because
the network will prevent it from ever being spent again. Those unspent outputs can now become
inputs for future transactions.
7. This is of course a very time consuming and inecient activity, e.g. Meiklejohn et al. (2013)by
participating in 344 transactions was able to manually tag 1,017 addresses.
8. A false positive exists when an address is wrongly included in a cluster (i.e. all addresses are not
controlled by the same entity), and a false negative when an address should be in a cluster but is
not.
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9. We elaborate on the rationale behind these ltering criteria later in the paper.
10. Indeed, fork-merge patterns and self-loops represent a frequent scenario in the Bitcoin economy,
e.g.?and ?.
11. Bitcoin Core was, by far, the most dominant versionoftheBitcoinblockchainoverthestudyperiod.
12. For example, p
Huobi
is the set of addresses associated to Huobi with n(p
Huobi
) = 37,756.
13. See Figure A2 in Appendix 1 for a visualization of the problem we aim at solving.
14. As an example, one of the biggest clusters holding about 6 million addresses which probably
should have been included in ^
CKis instead included in ^
CUbecause although it has 2 million
addresses linked to the MtGox exchange, it has one address linked to bitcoin 24.
15. Super clusters linked to the same real-world entity are merged into one node in the network.
16. For example, if the median value of intervals is 60 min, this means that counterparts tends to
send to cˆ
x
bitcoins every 60 min.
17. In order to capture exact density on point, a very precise width is needed. In this case, we set the
width 0.00000001 BTC (or 1 satoshi).
18. This is true, for example, in SatoshiDice, the largest bitcoin-based gambling service.
19. This practice is common in transactions involving small amounts of street drugs where a dime
bagis whatever quantity $10 buys and a nickel bagwhatever $5 buys.
20. This feature is consistent with the results in the former inow analysis.
21. For deeper insight into the detail algorithm please see ?.
22. One notable exception is on the 22 May 2010 in a purchase made by Laszlo Hanyecz, a software
developer who paid a fellow BitcoinTalk online forum user 10,000 BTC for two Papa Johns
pizzas. At todays prices that is the equivalent of $2.25m per pizza!
23. The authors use the terminology sincolloquially for illustrative purposes only, and do not
attribute any moral or ethical judgment to the word in the context of this paper.
24. For consistency, we also check other centrality measures like weighted degree and closeness, but the
result does not change. We refer the reader to ?for more details on network centrality measures.
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Koshy, P., Koshy, D. and McDaniel, P. (2014), An analysis of anonymity in bitcoin using p2p network
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Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., McCoy, D., Voelker, G.M. and Savage, S. (2013),
Astful of bitcoins: characterizing payments among men with no names,Proceedings of the
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Möser, M. and Böhme, R. (2017), Anonymous alone? Measuring Bitcoins second-generation
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Further reading
Barton, F., Himmelsbach, D., Duckworth, J. and Smith, M. (1992), Two-dimensional vibration
spectroscopy: correlation of mid-and near-infrared regions,Applied Spectroscopy, Vol. 46 No. 3,
pp. 420-429.
Blockchain.info (2015), available at: http://blockchain.info (accessed 1 June 2015).
Cormen, T.H., Leiserson, C.E., Rivest, R.L. and Stein, C. (2009), Data structures for disjoint sets,
Introduction to Algorithms, pp. 561-585.
Dingledine, E.A. (2004), The Second-Generation Onion Router, Naval Research Lab.
Hanneman, R. and Riddle, M. (2014), Introduction to Social Network Methods, University of California,
Riverside, available at: http://faculty.ucr.edu/hanneman/nettext
Walletexplorer (2015), available at: www.walletexplorer.com/ (accessed 1 June 2015).
Corresponding author
Paolo Tasca can be contacted at: p.tasca@ucl.ac.uk
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Appendix 1
Figure A1.
Structure of the
MySQL database
created from the data
described in Table I
Evolution of
the bitcoin
economy
123
Figure A2.
The total number of
clusters in the bitcoin
network is about 30
millionNotes: Our
research focuses on
2,850 large clusters
that include at least
100 addresses and
that also have
received at least 1,000
bitcoins from January
3, 2009 to May 8, 2015
(Known Group þ
Unknown Group)
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Appendix 2. List of identified super clusters in the set C
ˆK
Exchange 24850005 CoinSpot 45437770 BitYes 9549829 BitMillions 25855276 Evolution
Cluster ID Entity name 25671458 CoinTrader 45936035 Huobi 10352795 Betcoins 28893773 BlackBank
414294 VirWoX 25686021 Poloniex 46523945 CleverCoin 11592006 BTCOracle 32188483 CannabisRoad
725951 Cavirtex 25764718 LiteBit 47947669 BtcTrade 12554372 Coinroll 34450135 PandoraOpen
1152538 CampBX 26526196 AllCoin 48224085 BitVC 14592351 Just-Dice 39307422 MiddleEarth
1477742 MercadoBitcoin 26768188 VaultOfSatoshi 49491730 Coinmate 15935043 BIToomBa 53431789 Nucleus
1538322 BTCChina 27058962 MintPal 49497346 LocalBitcoins 17858445 YABTCL 58138309 Abraxas
1591210 Bitcash 27430132 C-Cex 51159703 Bter 18104553 BitZillions
1640270 BTC-e 27436957 Indacoin 52772381 ChBtc 18242583 Ice-Dice Others
1745068 Bitstamp 27516236 1Coin 63125407 BTCChina 18844372 SatoshiRoulette Cluster ID Entity name
1872280 Bitcoin 27883925 Bittrex 64576584 Exmo 19074264 Peerbet 51591631 HaoBTC
2403973 TheRockTrading 28195895 Paymium 64714050 HitBtc 20709464 Betcoin 2481605 BitcoinFog
3160452 OrderBook 29314526 AllCrypt 67252210 Bter 21368294 AnoniBet 38948179 BitLaunder
3169372 BitBargain 29907632 Dgex 74399779 MtGox 22181037 NitrogenSports 17685858 CryptoLocker
5128017 LocalBitcoins 30131409 CoinMotion 22815568 CoinGaming 1400957 MPEx
5946497 HappyCoins 30139877 Bter Mining pools 23210454 SatoshiBet 2062018 Bitcoinica
6299268 Cryptonit 30804162 CoinArch Cluster ID Entity name 24545072 999Dice 3165186 Bitcoinica
6606601 MtGox 30852641 BTCChina 177451 Eligius 24857474 BitcoinVideoCasino 32965397 UpDown
6960785 Bitnex 31778769 Coin-Swap 2400970 mining.bitcoin 26783278 PocketRocketsCasino 1406234 Btcst
7522909 Bitcoin-24 32344865 BitBay 2440660 BitMinter 28382823 BitAces 17144983 Purse
8058186 Justcoin 32394318 Bter 4886325 EclipseMC 32814149 BitStarz 21601241 Bylls
8764670 FYBSG 33419156 CoinCafe 5272039 GHash 33495508 Betcoin 14543862 Bitbond
11025414 BitX 34085743 BX 7530073 BTCGuild 37042731 CloudBet 36933042 BTCJam
11196419 SmenarnaBitcoin 34277949 BtcExchange 8388553 50BTC 38624871 PrimeDice 39317993 BitLendingClub
11749226 Cryptorush 35226292 MeXBT 11551066 50BTC 39363482 DiceNow 17815289 BTCt
12637441 McxNOW 35431781 Zyado 12547187 mining.bitcoin 41129839 DiceBitco 1075785 BitPay
12797521 Korbit 35636277 QuadrigaCX 13455133 KnCMiner 43427199 PrimeDice 16248472 CoinPayments
13228368 Vircurex 36674288 MaiCoin 18761724 CloudHashing 44125199 SatoshiMines 65645195 BitPay
13539065 Crypto-Trade 36837273 HitBtc 21224287 BTCChinaPool 45607266 FortuneJack 1582623 Bitmit
13549778 Cryptsy 37013580 Matbea 23855294 Polmine 48934666 SecondsTrade 13255854 CryptoStocks
14777694 Coins-e 37776533 Btc38 34581906 Genesis-Mining 50523669 Betcoin 454407 Instawallet
14833131 AnxPro 38951758 Ccedk 45656162 AntPool 52248120 SatoshiDice 869503 MyBitcoin
15004560 BitKonan 39963036 796 48150806 mining.bitcoin 57476416 BitcoinVideoCasino 8341192 Dagensia
16030982 OKCoin 40161739 LakeBTC 58048160 AntPool 58900551 PrimeDice 14011339 CoinJar
17494455 Huobi 41193900 Bitso 61166475 BW 64148592 BitAces 14359270 Xapo
(continued)
Table AI.
List of the super
clusters in C
K
. One
entity could own and
control more than
one cluster
Evolution of
the bitcoin
economy
125
17518823 CoinMkt 41323542 SpectroCoin 65420930 SwCPoker 14773742 Inputs
17747783 Kraken 41433875 OKCoin Gambling 73161189 PrimeDice 31631652 BitcoinWallet
18055670 Cex 41555907 BTC-e Cluster ID Entity name 51620287 OkLink
18847146 BtcMarkets 41614840 BTC-e 184867 Just-Dice Black Market 59825409 GoCelery
19681395 Bitcoin 41923963 Hashnest 1687007 BetsOfBitco Cluster ID Entity name
20789150 Coinomat 42369494 Cryptsy 2254800 SealsWithClubs 4401158 SilkRoad
21373812 Bleutrade 42879690 C-Cex 3486952 SatoshiDice 9563241 Sheep
21653414 Bitnex 43277175 Bit-x 4169604 BitZino 19517829 PandoraOpen
23421684 Coin 43970673 Bter 4831753 BtcDice 20627442 SilkRoad2
23672561 Masterxchange 43974172 Bter 8339663 BitEln 22735225 Agora
24089310 Igot 45333046 Bitcurex 9510403 Playt 22917766 BlueSky
Notes: The cluster IDs are generated internally from MySQL database, and each cluster has one unique cluster ID. Entities are classied according to
their business objective. We focus on the biggest four categories (exchange, mining pool, gambling, black market). Entities with exposure to more than
one category, such as HaoBTC (both wallet and mining pools) are categorized as composite
Table AI.
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... For further network analysis, it was necessary to transform the high-frequency algorithmic trading data. Our methodology followed the one given by Tasca et al. [3] , so to facilitate comparison, we had to convert minute values to daily values. Based on the daily values of "Exchange 1" and "Exchange 2", we created 455 daily matrices for each day from January 2019 to April 2020 in order to show the amount of arbitrage that was possible to earn by taking every arbitrage opportunity in each exchange. ...
... 1 We took data of each exchange, assigned required data format and using dpyr package grouped values by date and by "Exchange 1". 2 Using reshape2 package we changed the shape of our dataset to suitable one for the analysis. 3 We grouped data again by date to calculated the amount of the arbitrage in each exchange for every day. 4 We merged these values to show how much it could be earned each day if Bitcoin was bought in "Exchange 1" and simultaneously sold to specific "Exchange 2". ...
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Bitcoin market's efficiency and liquidity questions are being comprehensively analyzed in scientific literature. This dataset serves academics for deeper analysis of these topics as well as it gives relevant information for spotting and evaluating risks in the market. Moreover, practitioners can benefit from the dataset and use it to identify patterns in the market, discover potential earning capabilities, and create effective arbitrage trading strategies. This is the first publicly available dataset that provides unique arbitrage data about pairs of cryptocurrency exchanges. The raw dataset was received by the Bitlocus LT, UAB. Using dplyr, reshape2, plyr packages in R we transformed dataset to show the amount of arbitrage which could be earned in 13 different cryptocurrency exchanges from 2019-01-01 until 2020-04-01. We used this dataset to create matrices for each day from 2019-01-01 until 2020-04-01 in order to perform network analysis on Bitcoin arbitrage opportunities [1]. However, this dataset is beneficial for other purposes such as the evaluation of market's seasonality and day of week effects. The dataset provides values in high-frequency intervals but it is possible to convert data to a suitable data format depending on the research question.
... Our dataset has been pre-processed by Chainalysis Inc following approaches as in [73,74,75]. This process uses established heuristics [76,77,78,79,80] in order to map addresses into entities. This process is unsupervised, as there is no ground truth data to rely on. ...
Preprint
Online marketplaces are the main engines of legal and illegal e-commerce, yet the aggregate properties of buyer-seller networks behind them are poorly understood. We analyze two datasets containing 245M transactions (16B USD) that took place on online marketplaces between 2010 and 2021. The data cover 28 dark web marketplaces, i.e., unregulated markets whose main currency is Bitcoin, and 144 product markets of one regulated e-commerce platform. We show how transactions in online marketplaces exhibit strikingly similar patterns of aggregate behavior despite significant differences in language, lifetimes available products, regulation, oversight, and technology. We find remarkable regularities in the distributions of (i) transaction amounts, (ii) number of transactions, (iii) inter-event times, (iv) time between first and last transactions. We then show how buyer behavior is affected by the memory of past interactions, and draw on these observations to propose a model of network formation able to reproduce the main stylized facts of the data. Our findings have implications for understanding market power on online marketplaces as well as inter-marketplace competition.
... Chen et al. [5] characterized the illicit accounts involving in Ponzi scams with features extracted from the ether flow graph and opcodes. Tasca et al. [14] analyzed the transaction behaviors of four types of Bitcoin addresses, including exchange, miner, gambling, and black market. ...
Chapter
Ethereum is the largest blockchain system supporting Turing-complete smart contracts. In recent years, we have witnessed its boom and popularity in various applications. However, since users use pseudonyms in Ethereum, it is hard to know the true identity behind an account. Meanwhile, a large number of cyber-crimes in Ethereum emerged and have been reported. Therefore, it is an important task to analyze the transaction behavior of accounts in Ethereum and conduct account portraits based on the honest and public information which is transaction records. Although facing the anonymity challenge of blockchain, it makes this task possible that some Ethereum analysis platforms provide ground truth by classifying accounts into specific types. However, prior work tried to dig out features of one certain account type but lack of a comparative analysis of multi-class account types. In this paper, we model the partial Ethereum transaction data as a transaction network, then portray the characteristics of six types of accounts in Ethereum according to the obtained labels from both transaction statistics perspective and network structure perspective. Moreover, we adopt a Graph Convolutional Network (GCN)-based model to distinguish different kinds of accounts to verify the effectiveness of the properties we choose. The experimental results show that our model performs well in classifying various types of accounts in Ethereum.
Bitcoin is the largest blockchain, and provides the underlying UTXO architecture used by many other cryptocurrencies. We identify an inherent bias embedded in this architecture (the Notebreaker mechanism) which forces users to ‘spend’ the entire content of a wallet address in order to make a payment, receiving ‘change’ into a unique new address. This inflates both the apparent volume transacted and network users, as well as minimizing the apparent fees of transacting. We develop an innovative Transaction Identification Methodology (TIM) to quantify the economic value of transactions from raw blockchain data. Using four different algorithms across three stages, we achieve 95% accuracy in quantifying the degree of bias in these measures. Validated across more than 430 million Bitcoin transactions involving 600 million wallet addresses, our methodology reveals that the Notebreaker mechanism inflates transaction volumes 8 times, makes the actual costs of blockchain transactions appear 3-7 times more expensive than what is commonly reported, and inflates wallet counts – a common heuristic of unique adopter counts. We provide a remediation strategy to make Bitcoin blockchain data a more accurate representation of reality, and provide a daily data set of these remediated volumes and transaction fees.
Article
This study examines whether we can learn from the behavior of blockchain‐based transfers to predict the financing of terrorist attacks. We exploit blockchain transaction transparency to map millions of transfers for hundreds of large on‐chain service providers. The mapped dataset permits us to empirically conduct several analyses. First, we analyze abnormal transfer volume in the vicinity of large‐scale highly visible terrorist attacks. We document evidence consistent with heightened activity in coin wallets belonging to unregulated exchanges and mixer services – central to laundering funds between terrorist groups and operatives on the ground. Next, we use forensic accounting techniques to follow the trails of funds associated with the Sri Lanka Easter bombing. Insights from this event corroborate our findings and aid in our construction of a blockchain‐based predictive model. Finally, using machine‐learning algorithms, we demonstrate that fund trails have predictive power in out‐of‐the sample analysis. Our study is informative to researchers, regulators, and market players, in providing methods for detecting the flow of terrorist funds on blockchain‐based systems using accounting knowledge and techniques. This article is protected by copyright. All rights reserved
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Bitcoin is becoming a popular financial asset and means of transacting. However, little is known about an important aspect of the bitcoin market: its liquidity. We consider whether various dimensions of liquidity evident in other asset classes are present in bitcoin spot and futures liquidity. We find variations in spot liquidity across bitcoin exchanges and a strong commonality in bitcoin spot and futures market liquidity. The pricing of spot and futures bitcoin is relatively inefficient, and liquidity plays an important role. Deterioration in liquidity also contributes to bitcoin crash risk and large return declines. JEL Classification: G11, G23
Article
Purpose Blockchain is widely applied in e-voting, shared economy areas and other government functioning. Fragmented findings and distributed literature need consolidation for a holistic view of the research domain. The purpose of this study is to comprehensively reviews the blockchain applications for government organizations and presents the past, present and future trends of blockchain applications for government organizations. Design/methodology/approach Systematic review protocol instrumentalized the systematic review of research articles published from 2013 to 2021. Science mapping discerns scientific actors’ trends and performance analysis like most influential authors, documents and sources. Content analysis of selected data set unfolds the past, present and future of blockchain applications for government organizations. Findings Blockchain technology offers enormous potential for the transformation of government organizations and public services. The primary areas are cryptocurrency, e-voting, shared economy, smart contracts, financial and health services, tourism, logistics and water sustainability. Research limitations/implications This study reviewed only published research in journals and conference proceedings and excluded book reviews, book chapters and editorials from the review set. This study persuades governments and policymakers to invest in blockchain technology for transforming government organizations and public services. Practical implications This study highlights the importance of blockchain in government-controlled public departments, enhancing transparency and efficiency in public life. Social implications Blockchain technology enhances transparency, traceability and accountability of public records. Originality/value This study pioneers in chronologically highlighting the importance of blockchain in government-controlled public departments.
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This article focuses on the analysis of the potential of virtual currencies to contribute to global economic development, given their innovative characteristics and their rapid increase in popularity. This article intends to fill the gap in understanding the characteristics and the risks of the emergence of virtual currencies as part of the global financial systems. There is a huge potential of the new currencies backed by blockchain technology, to develop the existing payment systems and upgrade them, as they represent an innovative form of money with increased security of the transactions. For instance, the central banks of some of the world financial leaders, such as the US, UK, China, etc. have been experimenting with the possibility of integrating the technology behind the virtual currencies into the internal payment systems. In this sense, the governments are also working on creating a better legal framework to improve the recognition, licensing and registration of virtual currencies as an official form of payment, and also creating a better control mechanism of the new currency (Vejacka, 2014).
Chapter
The anonymity of blockchain has accelerated the growth of illegal activities and criminal behaviors on cryptocurrency platforms. Although decentralization is one of the typical characteristics of blockchain, we urgently call for effective regulation to detect these illegal behaviors to ensure the safety and stability of user transactions. Identity inference, which aims to make a preliminary inference about account identity, plays a significant role in blockchain security. As a common tool, graph mining technique can effectively represent the interactive information between accounts and be used for identity inference. However, existing methods cannot balance scalability and end-to-end architecture, resulting high computational consumption and weak feature representation. In this paper, we present a novel approach to analyze user’s behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern and effectively avoids computation in large-scale graph. Furthermore, we propose a generic end-to-end graph neural network model, named \(\text {I}^2 \text {BGNN}\), which can accept subgraph as input and learn a function mapping the transaction subgraph pattern to account identity, achieving de-anonymization. Extensive experiments on EOSG and ETHG datasets demonstrate that the proposed method achieve the state-of-the-art performance in identity inference.
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In this explorative study, we examine the economy and transaction network of the decentralized digital currency Bitcoin during the first four years of its existence. The objective is to develop insights into the evolution of the Bitcoin economy during this period. For this, we establish and analyze a novel integrated dataset that enriches data from the Bitcoin blockchain with off-network data such as business categories and geo-locations. Our analyses reveal the major Bitcoin businesses and markets. Our results also give insights on the business distribution by countries and how businesses evolve over time. We also show that there is a gambling network that features many very small transactions. Furthermore, regional differences in the adoption and business distribution could be found. In the network analysis, the small world phenomenon is investigated and confirmed for several subgraphs of the Bitcoin network.
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Bitcoin, the famous peer-to-peer, decentralized electronic currency system, allows users to benefit from pseudonymity, by generating an arbitrary number of aliases (or addresses) to move funds. However, the complete history of all transactions ever performed, called “blockchain”, is public and replicated on each node. The data it contains is difficult to analyze manually, but can yield a high number of relevant information. In this paper we present a modular framework, BitIodine, which parses the blockchain, clusters addresses that are likely to belong to a same user or group of users, classifies such users and labels them, and finally visualizes complex information extracted from the Bitcoin network. BitIodine labels users semi-automatically with information on their identity and actions which is automatically scraped from openly available information sources. BitIodine also supports manual investigation by finding paths and reverse paths between addresses or users. We tested BitIodine on several real-world use cases, identified an address likely to belong to the encrypted Silk Road cold wallet, or investigated the CryptoLocker ransomware and accurately quantified the number of ransoms paid, as well as information about the victims. We release a prototype of BitIodine as a library for building Bitcoin forensic analysis tools.
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What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we focus on Bitcoin, the most popular cryptocurrency. Bitcoin has experienced periods of rapid increase in exchange rates (price) followed by sharp decline; we hypothesise that these fluctuations are largely driven by the interplay between different social phenomena. We thus quantify four socio-economic signals about Bitcoin from large data sets: price on on-line exchanges, volume of word-of-mouth communication in on-line social media, volume of information search, and user base growth. By using vector autoregression, we identify two positive feedback loops that lead to price bubbles in the absence of exogenous stimuli: one driven by word of mouth, and the other by new Bitcoin adopters. We also observe that spikes in information search, presumably linked to external events, precede drastic price declines. Understanding the interplay between the socio-economic signals we measured can lead to applications beyond cryptocurrencies to other phenomena which leave digital footprints, such as on-line social network usage.
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Bitcoin is a purely online virtual currency, unbacked by either physical commodities or sovereign obligation; instead, it relies on a combination of cryptographic protection and a peer-to-peer protocol for witnessing settlements. Consequently, Bitcoin has the unintuitive property that while the ownership of money is implicitly anonymous, its flow is globally visible. In this paper we explore this unique characteristic further, using heuristic clustering to group Bitcoin wallets based on evidence of shared authority, and then using re-identification attacks (i.e., empirical purchasing of goods and services) to classify the operators of those clusters. From this analysis, we characterize longitudinal changes in the Bitcoin market, the stresses these changes are placing on the system, and the challenges for those seeking to use Bitcoin for criminal or fraudulent purposes at scale.
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The Bitcoin scheme is a rare example of a large scale global payment system in which all the transactions are publicly accessible (but in an anonymous way). We downloaded the full history of this scheme, and analyzed many statistical properties of its associated transaction graph. In this paper we answer for the first time a variety of interest-ing questions about the typical behavior of users, how they acquire and how they spend their bitcoins, the balance of bitcoins they keep in their accounts, and how they move bitcoins between their various accounts in order to better protect their privacy. In addition, we isolated all the large transactions in the system, and discovered that almost all of them are closely related to a single large transaction that took place in November 2010, even though the associated users apparently tried to hide this fact with many strange looking long chains and fork-merge structures in the transaction graph.
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This paper aims to identify the likely determinants for cryptocurrency value formation, including for that of bitcoin. Due to Bitcoin’s growing popular appeal and merchant acceptance, it has become increasingly important to try to understand the factors that influence its value formation. Presently, the value of all Bitcoins in existence represent approximately $7 billion, and more than $60 million of notional value changes hands each day. Having grown rapidly over the past few years, there is now a developing but vibrant marketplace for bitcoin, and a recognition of digital currencies as an emerging asset class. Not only is there a listed and over-the-counter market for bitcoin and other digital currencies, but also an emergent derivatives market. As such, the ability to value bitcoin and related cryptocurrencies is becoming critical to its establishment as a legitimate financial asset.
Conference Paper
Over the last 4 years, Bitcoin, a decentralized P2P crypto-currency, has gained widespread attention. The ability to create pseudo-anonymous financial transactions using bitcoins has made the currency attractive to users who value their privacy. Although previous work has analyzed the degree of anonymity Bitcoin offers using clustering and flow analysis, none have demonstrated the ability to map Bitcoin addresses directly to IP data. We propose a novel approach to creating and evaluating such mappings solely using real-time transaction traffic collected over 5 months. We developed heuristics for identifying ownership relationships between Bitcoin addresses and IP addresses. We discuss the circumstances under which these relationships become apparent and demonstrate how nearly 1,000 Bitcoin addresses can be mapped to their likely owner IPs by leveraging anomalous relaying behavior.
Conference Paper
Bitcoin is quickly emerging as a popular digital payment system. However, in spite of its reliance on pseudonyms, Bitcoin raises a number of privacy concerns due to the fact that all of the transactions that take place are publicly announced in the system. In this paper, we investigate the privacy provisions in Bitcoin when it is used as a primary currency to support the daily transactions of individuals in a university setting. More specifically, we evaluate the privacy that is provided by Bitcoin (i) by analyzing the genuine Bitcoin system and (ii) through a simulator that faithfully mimics the use of Bitcoin within a university. In this setting, our results show that the profiles of almost 40% of the users can be, to a large extent, recovered even when users adopt privacy measures recommended by Bitcoin. To the best of our knowledge, this is the first work that comprehensively analyzes, and evaluates the privacy implications of Bitcoin.
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