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Community discovery has emerged during the last decade as one of the most challenging problems in social network analysis. Many algorithms have been proposed to find communities on static networks, i.e. networks which do not change in time. However, social networks are dynamic realities (e.g. call graphs, online social networks): in such scenarios static community discovery fails to identify a partition of the graph that is semantically consistent with the temporal information expressed by the data. In this work we propose Tiles, an algorithm that extracts overlapping communities and tracks their evolution in time following an online iterative procedure. Our algorithm operates following a domino effect strategy, dynamically recomputing nodes community memberships whenever a new interaction takes place. We compare Tiles with state-of-the-art community detection algorithms on both synthetic and real world networks having annotated community structure: our experiments show that the proposed approach is able to guarantee lower execution times and better correspondence with the ground truth communities than its competitors. Moreover, we illustrate the specifics of the proposed approach by discussing the properties of identified communities it is able to identify.
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Mach Learn (2017) 106:1213–1241
DOI 10.1007/s10994-016-5582-8
Tiles: an online algorithm for community discovery
in dynamic social networks
Giulio Rossetti1,2·Luca Pappalardo1,2·
Dino Pedreschi1·Fosca Giannotti2
Received: 12 March 2015 / Accepted: 5 July 2016 / Published online: 8 September 2016
© The Author(s) 2016. This article is published with open access at
Abstract Community discovery has emerged during the last decade as one of the most chal-
lenging problems in social network analysis. Many algorithms have been proposed to find
communities on static networks, i.e. networks which do not change in time. However, social
networks are dynamic realities (e.g. call graphs, online social networks): in such scenarios
static community discovery fails to identify a partition of the graph that is semantically con-
sistent with the temporal information expressed by the data. In this work we propose Tiles,
an algorithm that extracts overlapping communities and tracks their evolution in time follow-
ing an online iterative procedure. Our algorithm operates following a domino effect strategy,
dynamically recomputing nodes community memberships whenever a new interaction takes
place. We compare Tiles with state-of-the-art community detection algorithms on both syn-
thetic and real world networks having annotated community structure: our experiments show
that the proposed approach is able to guarantee lower execution times and better correspon-
dence with the ground truth communities than its competitors. Moreover, we illustrate the
specifics of the proposed approach by discussing the properties of identified communities it
is able to identify.
Keywords Community discovery ·Dynamic networks ·Social network analysis
Guest Editors: Céline Rouveirol, Rushed Kanawati, and Ruggero G. Pensa.
BGiulio Rossetti;
Luca Pappalardo
Dino Pedreschi
Fosca Giannotti
1KDD Lab, University of Pisa, Pisa, Italy
2KDD Lab, ISTI-CNR, Pisa, Italy
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1214 Mach Learn (2017) 106:1213–1241
1 Introduction
Community Discovery has become during the last decade one of the most challenging and
studied problems in social network analysis due to its relevance for a wide range of appli-
cations: from the study of information and disease spreading (Wu and Liu 2008;Bhat and
Abulaish 2013) to the compression of networked data (Buehrer and Chellapilla 2008), the
prediction of future interactions and activities of individuals (Rossetti et al. 2015a,b), and
even the analysis of the patterns of human mobility (Rinzivillo et al. 2012;Bagrow and
Lin 2012. Even though several definitions of network communities exist, the common sense
depict such mesoscale substructures as sets of nodes that are closer or more similar to each
other than to anybody else outside the community. Several community discovery algorithms
have been proposed to deal with static networks, i.e. networks which do not change their
topology in time (Fortunato 2010;Coscia et al. 2011). These algorithms work relying on the
so-called quasi-steady state assumption (QSSA): they model networks as “frozen in time” by
leveraging the observation that mutations in their topology happen only in the long run. This
assumption simplifies the algorithmic design but often leads to biased results when analyzing
peculiar network typology, such as social networks, which are by their nature highly dynamic:
indeed, online interaction networks, call graphs, buyer-seller transactions are all examples
of rapidly and continuously changing systems for which a QSSA does not apply. To clarify
this point, consider the network scenario depicted in Fig. 1: a partition extracted by a static
community discovery algorithm, that usually looks only at the final state of the network (e.g.,
t=5 in this example), without taking into account the temporal ordering of interactions, it
can group together nodes that have been in contact rarely and whose interactions can be very
distant in time. Conversely, dynamic approaches can propose multiple time-aware partitions
following different criteria: for instance a dynamic algorithm that search for -communities
(i.e., set of nodes connected by edges whose timestamp differ for at most ) can identify
multiple, possibly overlapping, sub-structures such as, assuming =1, {u,x,y}, {u,v,y,j}
and {v,z,j}. Following an alternative view, an online approach can smooth the community
evolution by identifying incrementally the boundaries each sub-structure has as the network
evolves through time.
In dynamic networks, the rise of new nodes and edges produces deep topological mutations
and creates new paths connecting once disconnected components. Therefore, an algorithm
that considers social networks as static entities—frozen in time—necessarily introduces bias
on its results. For these reasons, we advocate the need to weaken the QSSA and imagine social
(a) (b) (c)
xy j
vu 3
xy j
vu 3
Fig. 1 Communities identified by a dynamic community discovery algorithm. Numbers on the edges represent
interaction times. A static algorithm, working on the final graph (c), identifies just one community, since it
does not take into account the network evolution. In contrast, looking at the same network through a dynamic
perspective we are able to unveil different communities describing different stages of its evolution: at time
t=2 we have the community C0={u,x,y}(a); at time t=3 a new community C1={v, z,j}appears (b)
and at time t=5 all nodes are part of a single community (c)
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Mach Learn (2017) 106:1213–1241 1215
networks as complex, mutable, evolving objects which change in an fluid manner every time
a new interaction appears (or disappears). In this scenario, pursuing an online community
discovery approach enables valuable complementary benefits such as: (1) the reduction of
computational complexity (both in space and time), (2) the tracking of community dynamics,
(3) the possibility to feed predictive models with punctual and fine grained information
regarding how the network topology changes over time.
Nonetheless, a dynamic approach to community discovery enables interesting practical
applications. For instance, time-aware approaches can be used by mobile phone carriers that
want to propose a flexible billing plan for their customers by lowering call prices to users
in the same social circle: indeed, when imposing a fixed network structure such marketing
strategy looses its effectiveness since static communities overestimate or underestimate the
real connectivity (Fig. 1). In contrast, a dynamic community discovery algorithm provides
up-to-date communities and helps the company in providing its users with a more customized
In this work, we propose to adapt the classical community discovery problem to the
dynamic scenario and introduce an evolutionary formulation able to deal with evolving
networks. This dynamic perspective on the community discovery problem allows us to inves-
tigate, describe and quantify relevant processes that take place on social networks, such as
the evolution through time of the network community structure, the evolution through time
of each single community both in terms of topology and events (birth, growth, death etc.) and
even the evolution of single individuals connections within different communities. Moreover,
we propose Tiles,1an algorithm that tracks the evolution of communities through time. Our
approach proceeds in a streaming fashion considering each topological perturbation as a fall
of a domino tile: every time a new interaction appears in the network, Tiles first updates the
communities locally and then propagates the changes to the node surroundings adjusting the
neighbors’ community memberships. The online nature of Tiles brings many advantages.
First, the computation of network sub-structures is local and involves a limited number of
nodes and communities, thus speeding up the updating process. Second, our approach allows
to observe two types of evolutionary behaviors: (1) the stability of individuals’ affiliations to
communities, and (2) the evolution over time of interaction-based communities.
We validate the effectiveness of our algorithm by comparing it with state-of-the-art com-
munity discovery algorithms, using both synthetic and real networks enriched by ground
truth communities. In our experimental analysis we underline that Tiles is able to achieve
a better match with the ground truth communities than the compared algorithms. Moreover,
we show that Tiles guarantees lower execution times than the competitors since it can be
easily parallelized. We also provide a characterization of the communities extracted by our
algorithm by analyzing three Big Data sources: a nation-wide call graph of one million users
whose interactions are tracked for one month; a Facebook interaction network which covers
a period of 52 weeks; an interaction network of 8 million users of the Chinese microblogging
platform WEIBO observed for 1 year.
The paper is organized as follows. Section 2summarizes the related works in commu-
nity discovery, dynamic network analysis and evolutionary community discovery; Section 3
formalizes the problem of Evolutionary Community Discovery. Section 4describes Tiles
providing algorithmic details and showing some characteristics of the algorithm. In Sect. 5
we compare Tiles with other community detection algorithms and present a characterization
of discovered communities. Finally, Sect. 6concludes the paper, describing some scenarios
for future works.
1Temporal Interactions a Local Edge Strategy.
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1216 Mach Learn (2017) 106:1213–1241
2 Background and related works
The problem of finding and tracking communities in an evolutionary context is relatively
novel. Here we discuss some relevant works regarding classical community discovery,
dynamic networks analysis and, as their merging point, evolutionary community discovery.
2.1 Community discovery
The problem of finding communities in complex networks is a hot topic, as witnessed by
the high number of works in this field. A survey by Fortunato (2010) explores all the most
popular techniques to find communities in complex networks. The more recent survey by
Coscia et al. (2011) tries to classify families of algorithms based on the typology of the
extracted communities. The classic definition of community relates to a dense subgraph,
in which the number of edges among its nodes is significantly higher than the number of
outgoing edges. However, this definition does not cover many real world scenarios, and many
different alternative definitions of communities have been proposed. One of the most famous
is based on the modularity concept, a quality function of a graph partition proposed by Clauset
et al. (2004), which scores high values for partitions whose internal cluster density is higher
than the external density. An alternative approach is the application of information theoretic
techniques, as for example in Infomap (Rosvall and Bergstrom 2008). An interesting property
for community discovery is the ability to return overlapping sub-structures, i.e., to allow
nodes to be part of more than one community. This property reflects the social intuition
that each person is part of multiple different communities (e.g. work, family, hobby…). A
wide set of algorithms were developed over this property, such as the one proposed in Palla
et al. (2005). Other overlapping approaches are based on Label Propagation such as Demon
Coscia et al. (2012), a framework which allows a bottom–up formation of communities
exploiting ego-networks. Given the rising interests on multiplex (multidimensional/multi-
relational) networks, recently some community discovery algorithms able to partition labeled
multigraph have been proposed (Boden et al. 2012).
2.2 Dynamic network analysis
Several graph problems are, by their nature, closely tied to network dynamics (Kostakos
2009). The flowing of time plays different roles over a complex network: it can determine
the evolution of the graph topology (e.g. edges and node can fall and rise, communi-
ties born and die) or lead to the observation of diffusion processes. Among the problems
related to network evolution, Link Prediction is one of the most studied: formulated by
Nowell and Kleinberg (2003) its aim is to predict edges that will appear in the future given
the actual state of the network. Models for network growth, as the ones proposed in Barabási
and Albert (1999)andLeskovec et al. (2005), replicate network evolutions peculiarity in
order to build synthetic graphs. Furthermore, diffusion processes have been studied in order
to understand virus epidemics (Wang et al. 2009) and spreading of innovations (Burt 1987).
2.3 Evolutionary community discovery
Communities are certainly the mesoscale structures most affected by changes in network
topology: as time goes by the rise and fall of nodes and edges determines the appearance
and vanishing of social clusters that static community discovery algorithms are unable to
detect. In order to understand how communities evolve, three main approaches have been
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Mach Learn (2017) 106:1213–1241 1217
followed so far: Independent Community Detection and Matching, Global Informed iterative
community detection, and Local Informed iterative community detection. In the following,
we report a survey on these categories of approaches.
2.3.1 Independent community detection and matching
Strategies that fall in this category are prevalently aimed to track the evolution of communi-
ties by identifying key actions which regulate their life (birth, death, merge, split). Nguyen
proposes an extended life-cycle model able to track, in an offline fashion, the evolution of
communities (Nguyen 2012). Such methodology, as well as the one introduced in Goldberg
et al. (2011), Dhouioui and Akaichi (2014), Takaffoli et al. (2014)andAsur et al. (2009),
works on a two-step procedure: (1) the graph is divided in ntemporal snapshots and, for each
of them, a set of communities is extracted; (2) for each community an evolutionary chain is
built by observing its evolution through temporal adjacent sets. In their work, Takaffoli et al.
introduced Modec (Takaffoli et al. 2011), a framework able to model and detect the evolution
of communities obtained at different snapshots in a dynamic social network. The problem
of detecting the transition of communities is solved by identifying events that characterize
the changes of the communities across time. Unlike previous approaches (Palla et al. 2007)
the Modec framework is independent from the static community mining algorithm chosen
to partition time-stamped networks.
2.3.2 Global informed iterative community detection
A different methodology to detect communities in a dynamic scenario, is to design a proce-
dure where each community identified at time tis influenced by the ones detected at time
t1 avoiding the need to match communities, thus introducing global smoothness in the
community identification process. The approaches belonging to this category derive from
the evolutionary clustering analysis (Chakrabarti et al. 2006). Folino and Pizzuti (2014)pro-
pose an evolutionary multi-objective approach to community discovery in dynamic networks
which, moving from an evolutionary clustering perspective, searches for smooth community
transitions among consecutive time steps. Rozenshtein et al. (2014) focus on identifying
the optimal set of time intervals to discover dynamic communities in interaction networks.
Although those approaches reduce the complexity of the matching phase, they are based on
a static temporal partition of the complete temporal network. Other works belonging to this
category are Sun et al. (2010), Shang et al. (2012)andGuo et al. (2014).
2.3.3 Local informed iterative community detection
The last category, also known as online approaches, is defined by algorithms that do not
partition the full temporal annotated graph, but try to build and maintain communities in
an online fashion following the rising and vanishing of new nodes and edges. Only a few
works, at the best of our knowledge, have exploited this strategy so far. Qi et al. (2013)pro-
pose a probabilistic approach to determine dynamic community structure in a social sensing
context. The main objective of the introduced IC- DRF model is to dynamically maintain
a community partition of moving objects based on trajectory information up to the current
timestamp. However, due to the information used to update the community membership, the
approach is suitable only for a specific kind of networked data. Lin et al. (2008) propose an
iterative algorithm that, avoiding the classical two-step analysis, extract communities tak-
ing care of the topology of the graph at the specific time frame tas well as the historical
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1218 Mach Learn (2017) 106:1213–1241
evolutive patterns of previously computed communities. In Cazabet et al. (2010), Cazabet
introduces iLCD an overlapping online approach to community detection which re-evaluates
communities at each new interaction according to the path lengths between each node and
its surrounding communities. Xu et al. (2013) propose an algorithm aimed at analyzing the
evolution of community cores. The proposed approach tracks only stable links within face to
face interaction graphs exploiting a rule based on-line approach. Other works belonging to
this category are Zakreweska and Bader (2015), Lee et al. (2014)andNguyen et al. (2011).
Tiles belongs to the latter family of approaches. However, unlike the previously mentioned
algorithms, it uses only local topological information and a constrained label propagation in
order to minimize the computation needed to maintain updated the community structure.
3 Evolutionary community discovery
The overwhelming number of papers proposed in recent years clearly expresses that
researchers are not interested in formulating “The Community Discovery algorithm” but
in finding the right algorithm for each specific declination of the problem. Moving from this
observation we tackle a specific and not yet deeply studied problem: evolutionary community
discovery in dynamic social networks.
Definition 1 (Evolutionary Community Discovery) Given an interaction streaming source
Sand a graph G=(V,E),whereeEis a triple (u,v,t)with u,v Vand tNis
the time of the interaction’s generation by S, the Evolutionary Community Discovery (ECD)
aims to identify and maintain updated the community structure of Gas new interactions are
generated by S.
The source Sproduces new interactions among pair of nodes which can be either already
part of the graph or newcomers. It models scenarios in which interactions do not occur with
a rigid temporal discretization but flow “in streams” as time goes by. After all, this is how
social interactions actually take place: phone calls, SMS messages, tweets, Facebook posts are
produced in a fluid streaming fashion and consequently the corresponding networks’ social
communities also change fluidly over time. In contrast with a static community detection
algorithm, an ECD algorithm must produce a series of communities’ observations in order
to describe how time shapes network topologies in coherent substructures. Moreover, an
ECD algorithm should address the following question: given a community Cat timestamp
tand a streaming source S, what its structure will be at an arbitrary time t+given the
interactions produced by S? In order to answer this question the discovery process must be
able to smooth the evolution of a community from tto t+by identifying its local mutations
avoiding external matching as done by traditional two-step approaches. Our algorithm, Tiles,
is designed to solve the ECD problem since it tracks the evolution of communities following
a domino tile strategy.
4Tiles algorithm
Social interactions determine how communities form and evolve: indeed, the rising and
vanishing of interactions can change the communities’ equilibrium. A common approach in
literature to address topology dynamics is to: (1) split the network into temporal snapshots,
(2) repeat a static community detection for each snapshot and (3) study the variation of
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Mach Learn (2017) 106:1213–1241 1219
the results as time goes by. This approach introduces an evident issue: which temporal
threshold has to be chosen to partition the network? This problem, which is obviously context
dependent, also introduces another one: once the algorithm is performed on each snapshot
how can we identify the same community in consecutive time slots? To overcome these
issues we propose Tiles, an ECD algorithm that does not impose fixed temporal thresholds
for the partition of the network and the extraction of communities. It proceeds analyzing an
interaction stream: every time a new interaction is produced by a given streaming source,
Tiles uses a label propagation procedure to diffuse the changes to the node surroundings
and adjust the neighbors’ community memberships. A node can belong to a community with
two different levels of involvement: peripheral membership and core membership. If a node
is involved in at least a triangle with other nodes in the same community it is a core node
while if it is an one-hop neighbor of a core node it is a peripheral node. Only core nodes
are allowed during the label propagation phase to spread community membership to their
neighbors. Tiles generates overlapping communities, i.e. each node can belong to different
communities which can represent the different spheres of the social world of an individual
(friendship, working relations, etc.).
The algorithm2takes as input four parameters: (1) the graph G, which is initially empty;
(2) an edge streaming source S;(3)τ, a temporal observation threshold; (iv) a Time To Leave
(ttl) value for the interactions. The temporal observation threshold τspecifies how often we
want to observe the structure of the communities allowing us to customize the output of the
algorithm. Furthermore, ttl models the expected lifespan of a new interaction: it acts as a
temporal decreasing countdown that, when expired, leads to the removal of the edge it is
attached to. Indeed the value of ttl impacts the overall stability of the observed phenomena.
Studying a dynamic network we can model its evolutionary behavior to comply with one of
the following general scenarios: (a) accumulative growth or (b) limited memory growth. The
former assumes that once an interaction among a pair of nodes takes place it is permanent; the
latter states that interactions gradually lose their strength as time goes by till they disappear:
ttl is introduced to interpolate these two behaviors and indicates the time after which an
edge (u,v) decays if there were no interactions between nodes uand v. The output of the
algorithm is a chronologically ordered sequence of community sets, each set representing the
network partition as it appears at the end of an interval of size τ. Each community within a
set is composed by two groups of nodes: the ones belonging to the community periphery and
the ones belonging to the community core. Setting τequals to the streaming source clock
ensures the output of community status every new network update.
Algorithm 1shows the behavior of Tiles. First of all a new interaction e=(u,v)generated
by the source Sis added to the graph (lines 3–8). Then the following scenarios are considered:
1. both nodes uand vappear for the first time in the graph. No other actions are performed
until the next interaction is produced by the source S(Fig. 2a, Algorithm 1lines 10–12);
2. one node appears for the first time and the other is already existing but peripheral or
both nodes are existing but peripheral, in any case they do not belong to any community
core. Since peripheral nodes are not allowed to propagate the community membership,
no action is performed (none of the “if” clauses is satisfied) until the next interaction is
produced by the source S(Fig. 2a, Algorithm 1lines 14–18);
3. one node appears for the first time in Gwhile the other is an already existing core node.
The new node inherits a peripheral community membership from the existing core node
(Fig. 2b, Algorithm 1lines 20–23);
2Tiles Python implementation available at:
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1220 Mach Learn (2017) 106:1213–1241
vu y
u v
z v
Fig. 2 Community updates at the appearance of a new edge. Dashed black lines identify new interactions;
dashed line rectangles identify the periphery of communities; continuous line rectangles identify core com-
munities. aBoth nodes appears for the first time (blue) or one of them already exists but is not core (red): a
peripheral community is created. bA new node vjoins a core community and becomes part of its periphery;
ca new interaction emerges between nodes uand vwhich are core nodes in different communities: the two
nodes become peripheral nodes of the other’s node core community. dA node, core for a community and
periphereal in a different one, becomes core in the latter (Color figure online)
4. both nodes are core nodes already existing in G(Algorithm 1lines 25–33). In this case
we have two possible sub-scenarios:
(a) Nodes uand vdo not have common neighbors (we identify as (u)the set of
neighbors of u): they propagate each other a peripheral community membership
through the PeripheralPropagation procedure (Fig. 2c, Algorithm 1lines 27–
(b) Nodes uand vdo have common neighbors: their community memberships are
re-evaluated and the changes propagated to their surroundings by the CorePropa-
gation function (Fig. 2c, d, Algorithm 1lines 30–32).
Tiles’s communities grow gradually expanding their core and their peripheries through the
PeripheralPropagation (see Algorithm 2)andtheCorePropagation (see Algorithm 3)
The PeripheralPropagation procedure regulates the events where a new node becomes
part of an already established community. Since, initially, the newcomer is not involved in
any triangle with other nodes of the community it becomes part of its periphery. The same
function is performed when a new interaction connects existing nodes that do not share any
The CorePropagation procedure assumes that nodes uand vhave at least one common
neighbor z. For each triple (u,v,z)if at least two nodes are core for the same community
the third one becomes core as well (Fig. 3c, d Algorithm 3lines 8–16), otherwise a new
community is created upon the new triangle (Figure 3a, b, Algorithm 3lines 2–6). Once
the core nodes are established they propagate a peripheral membership to their neighbors,
if they are not already within the community. In Algorithm 3, the operator C(·)is used to
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Mach Learn (2017) 106:1213–1241 1221
Fig. 3 Example of Tiles community growth. We consider four consecutive updates extracted from a Facebook
interaction network, each new interaction is depicted with a red dashed line.Colored shapes identify core
communities. Nodes with solid borders outside the colored shapes are the peripheral nodes. Nodes with dashed
borders outside the colored shapes are not involved in any community (Color figure online)
define the intersection of the communities of the nodes passed as parameters. Tiles imposes
a single condition for a node uto be in a community core:itmustbeinvolvedinatleasta
triangle with others in the same community core. This choice guarantees an overall tightness
of the observed topologies and avoids the presence of chain-like communities which, in our
opinion, are not realistic structures in real social contexts.
4.1 Expired edges removal
We introduce a removeExpiredEdges procedure (Algorithm 4) to allow edges to decay.
As we discussed, Tiles execution is parametric on ttl (time to live) value for interactions:
when ttl =0 an edge disappears immediately after its rising producing an empty network
at each new step; when ttl =+we fall in the accumulative growth scenario. Finally, if
ttl (0,+∞)each edge decays after a ttl time from its generation by the streaming source S.
In order to reduce the complexity of the removal phase the interactions are stored in an external
priority queue (RQ in Algorithms 1and 4) and retrieved chronologically from the oldest to
the newest. Moreover if an existing interaction is refreshed before it expires we only update
its priority in the queue restarting its ttl (Algorithm 1line 4). In removeExpiredEdges
(Algorithm 4) we analyze each interaction (u,v,t)present in QR and handle two scenarios:
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1222 Mach Learn (2017) 106:1213–1241
Algorithm 1 Tiles(G,S,τ,ttl)
Require: G: undirected graph, S: streaming source, τ: temporal observation threshold,ttl: edges time to leave
1: actualt=0,RQ={}
2: while S.isActive()do
3: (u,v,t)S.getNewInteraction()
4: RQ.Update(u,v,t)
5: G.removeExpiredEdges(RQ, ttl,actualt)
6: if (u,v) /Gthen
7: G.addEdge(e)
8: end if
10: if |(u)|==1 and |(v)|==1 then (1)
11: Continue
12: end if
14: coreu=G.GetCommunityCore(u)(2)
15: corev=G.GetCommunityCore(v)
16: if coreu== and corev== then
17: Continue
18: end if
20: if |(u)|==1 and |(v)|>1then (3)
21: G.PeripheralPropagation(u,{v})
22: else if |(u)|>1and |(v)|==1 then
23: G.PeripheralPropagation(v, {u})
25: else (4)
26: CN = (u)(v)
27: if |CN|== 0 then (4a)
28: G.PeripheralPropagation(u,{v})
29: G.PeripheralPropagation(v, {u})
30: else (4b)
31: G.CorePropagation(u,v,CN)
32: end if
33: end if
35: if t-actualt== τthen
36: OutputCommunities(G)
37: actualt=t
38: end if
39: end while
Algorithm 2 PeripheralPropagation(u,nodes)
Require: u: node of G, nodes: a set of nodes
1: for vnodes do
2: for cG.GetCommunityCore(u)do
3: G.AddToCommunityPeriphery(v, c)
4: end for
5: end for
1. (u,v,t)is expired: the edge is removed from the graph and from RQ (Algorithm 4, lines
2. (u,v,t)is still valid: since the interactions in RQ are ordered the execution of remove-
ExpiredEdges is terminated (Algorithm 4, line 19).
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Mach Learn (2017) 106:1213–1241 1223
Algorithm 3 CorePropagation(u,v,CN)
Require: u,v: nodes of G, CN: u & v common neighbors in G.
1: for zCN do
2: if C(u,v,z) =∅then
3: G.CreateNewCommunity(u,v,z)New Community
4: PeripheralPropagation(u,(u))Periphery expansion
5: PeripheralPropagation(v, (v))
6: PeripheralPropagation(z,(z))
8: else if C(u,v) =∅then
9: G.AddToCommunityCore(z,C(u,v))Core expansion
10: PeripheralPropagation(z,(z))Periphery expansion
11: else if C(u,z) =∅then
12: G.AddToCommunityCore(v, C(u,z))
13: PeripheralPropagation(v, (v))
14: else if C(z,v) =∅then
15: G.AddToCommunityCore(u,C(z,v))
16: PeripheralPropagation(u,(u))
17: end if
18: end for
Algorithm 4 removeExpiredEdges(RQ, ttl,actualt)
Require: RQ: a priority queue containing the edge candidate to be removed, ttl: edges time to live, actualt:
actual timestamp.
1: for (u,v,t)in RQ do
2: if (actualt-t)ttl then (u,v) ttl is expired
3: G.removeEdge(u,v)
4: RQ.remove(u,v)
5: to_update = {(u)(v)}∪{u,v}
6: for community C(u,v) do
7: components = G.getComponents(community)
8: if |components|== 1 then
9: G.UpdateNodeRoles(community, to_update)
10: else
11: for ccomponents do Handling splits
12: sc = G.NewCommunity(c)
13: G.RemoveNodes(community, Vc)
14: G.UpdateNodeRoles(sc, c)
15: end for
16: end if
17: end for
18: else
19: return No more expired edges
20: end if
21: end for
The removal of an interaction (case 1) affects the communities shared by its endpoints:
in particular, the removal of (u,v,t)produces the re-evaluation of community memberships
for nodes u,vand their first level neighbors. Two scenarios can occur after such events:
(a) the original community is not “broken”, i.e. it is still composed by a single component:
we need only to re-evaluate the “roles” of nodes u,vand their first level neighbors
(Algorithm 4, lines 8–9);
(b) the original community is splitted into two or more separate entities: each component is
then considered as a new community and the “roles” of nodes are recomputed (Algorithm
4, lines 10–16).
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1224 Mach Learn (2017) 106:1213–1241
Algorithm 5 UpdateNodeRoles(c, to_update)
Require: c: community, to_update: set of nodes.
1: for node to_update do
2: if ClusteringCoefficient(node, c)>0then
3: ccore =ccore ∪{node}Add node to the core
4: else
5: if node ccore then
6: ccore =ccore −{node}Remove node from the core
7: cperi phery =cper ipher y ∪{node}Add node to the periphery
8: G.RemovePeriphery(node, c) Clean the first level neighborhood
9: end if
10: end if
11: end for
y j m
y j
Fig. 4 Example of expired edge removal. aA community where the edge (v, j)is candidate for removal
(the dashed line). bThe updated community: node jand yleave the community core because they are not
involved in any triangle with core nodes (clustering coefficient equals to 0); node kand mleave the community
periphery due to the propagation phase
In order to assign each node to the periphery or the core of a community we define the
UpdateNodeRoles procedure (Algorithm 5).
UpdateNodeRoles analyzes the local clustering coefficient (CC) of each node within the
specific community and retains as core nodes the ones with CC >0 (Algorithm 5, lines 2–3)
and as peripheral nodes the ones with CC =0 (Algorithm 5, lines 4–9). Once ensured that
the node roles within the modified community are consistent, a propagation is performed on
the neighborhood of nodes which moved from the core to the periphery (Algorithm 5, line 8):
peripheral nodes attached only to the demoted core node are removed from the community.
Figure 4shows an example of edge removal scenario.
Even if in the proposed formulation the interaction removal is performed through a fixed
size sliding window controlled by the ttl parameter, Tiles can be easily parametrized to
allow custom removal strategies. For instance, we can substitute the interaction validity
check (Algorithm 4line 2) with a decay function3or to handle directly interaction removal
as done for the insertion, i.e. leveraging if available in the data explicit information on edge
vanishing. Certainly this latter scenario represents the optimum since the analyst does not
need to define arbitrary thresholds and/or make additional assumptions. We choose to adopt
a sliding window in order to provide a simple and tunable way to simulate asynchronous
updates, an approach often used when dealing with temporal annotated data streams.
3The analysis of the distribution of interaction inter-arrival times is a very challenging topic, see Passarella
et al. (2011), Boldrini et al. (2011), Burt (2000)andGoh and Barabási (2008).
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Mach Learn (2017) 106:1213–1241 1225
4.2 Computational complexity of Tiles
Since Tiles operates on streaming data its complexity analysis depends on two main opera-
tions: interaction insertion and removal.
4.2.1 Interaction insertion phase
Interaction insertion causes perturbations on the network topology and it can induce updates
on the community structure. As shown in Algorithm 1there are five mutually exclusive rules
that apply when a new interaction (u,v,t)arises:
both uand vappear for the first time: no action taken, so complexity O(1);
at least one node was already present in the network but both nodes are not core: no
action taken, so complexity O(1);
node uis core in one or more communities and node vis new: PeripheralPropagation
is called on the new node: the function cycles on the communities for which uis core to
perform the propagation on v, so complexity O(core(v)) < O(|V|);
uand vare both core nodes for one or more communities that do not share neigh-
bors: PeripheralPropagation is called on both nodes, so complexity O(core(v) +
core(u)) < O(|V|);
uand vare core nodes for one or more communities that share neighbors: CoreProp-
agation is called. This function cycles over the common neighbors of uand v,which
in the worst case scenario are all the nodes of the network (O(|V|)), updates the com-
munity cores and performs PeripheralPropagation O(core(v) +core(u)). Thus the
final complexity is O(|V|∗(core(u)+core(v))).
In the worst case scenario this step has complexity O(|V|∗(core(u)+core(v))) since
the most costly rule is applied when the edge endpoints uand vare core nodes that share
|V|neighbors. However, reaching this upper bound is unusual due to the power law degree
distribution which characterizes real world interaction networks (it applies only for few hubs
in the network).
4.2.2 Interaction removal phase
The interaction removal phase complexity can be decomposed in two steps:
1. Main loop on the removal queue RQ (Algorithm 4). The cycle is executed until a valid
interaction is found:
–ifttl =0 (zero-memory scenario) it consumes all the edges in RQ: therefore, we
have O(|E|)cycles;
–if0<ttl <,|RQttl|is the expected average size of the interactions processed
when removeExpiredEdges is called: we have O(|RQttl|)<O(|E|)cycles;
–ifttl =∞(full memory scenario), the removal is not executed at all, thus the
complexity is O(1).
2. Node role update (Algorithm 5). The main computational cost here is due to the clustering
coefficient computation for each of the selected nodes on the community-induced graph.
A naive implementation, assuming a complete clique, has cubic cost on the cardinality
of nodes to be updated O(|to_update|3). Interaction networks are sparse so, in order
to provide a more realistic complexity, we can assume that that every node in the set
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1226 Mach Learn (2017) 106:1213–1241
has |to_update|neighbors within the community: thus, we can estimate the overall
complexity with O(|to_update|2)∗|to_update|=O(|to_update|2).
Considering the common scenario where 0 <ttl <, the final complexity is O(|RQttl|∗
|to_update|2). Moreover, it is worth noting that as ttl 0, |RQttl|becomes small because
each interaction is removed right after its appearance, while when ttl →∞it increases its
size. In the latter case the removal phase is executed rarely or not executed at all when ttl
exceeds the observation period available for the data.
4.3 Tiles properties
Given its streaming nature Tiles shows two main properties: (1) it can be used incremen-
tally on a precomputed community set; (2) it can be parallelized if specific conditions are
satisfied. Moreover, in presence of a deterministic interaction source S(i.e., a generator that
always produces the same ordered sequence of interactions), the output of Tiles is uniquely
determined. In this section we discuss and formalize such characteristics.
4.3.1 Incrementality
As specified above, Tiles is called on an initially empty graph. However, it also works on non-
empty graphs whose nodes are assigned to valid Tiles communities. Given a deterministic
streaming source Stat time tand a non-empty graph Gt, whose nodes are assigned to a
(valid) Tiles community set Ct=(c1,c2,...,cn), the following property holds:
where Gis an empty graph, S0is the streaming source Sat the initial time t=0. In Equation 1
the TILESfunction takes as inputs a graph and a streaming source and returns the partition
computed once the stream ends. Since Tiles updates the partition incrementally the final
communities produced starting with the source Son a given graph Gat time 0 or at time t
are identical – assuming Sdeterministic.
Incrementality is a property that an online algorithm operating on streaming data must
satisfy: it assures that every new network perturbation produces updates in the community
status and that the computation proceeds smoothly one interaction after the other. Moreover
it ensures that the approach does not require external community matching across time as
done by two-step approaches. Incrementality, if the stream source is deterministic, imposes
that the final partition is univocally determined. In Tiles this is ensured by construction by
the mutual exclusivity of the update rules: given a new edge and a given network status only a
single pattern among the ones described can be executed (both for insertion and deletion). On
the other hand in case of a non-deterministic streaming source, incrementality guarantees the
smoothness of the updates but not that the final partition will be always the same (i.e., given
a set of interactions generated with a different ordering w.r.t. a previous Tiles execution it is
not assured to reach the same final partition).
4.3.2 Compositionality
Tiles is parallelizable by identifying disjoint streams of edges produced by the deterministic
source S.GivenagraphG, and two disjoint streams of edges Si,Sii iff (u1,v
2)Sii:(C(u1)C(v1)) (C(u2)C(v2)) =∅,whereC(·)returns the set of
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Mach Learn (2017) 106:1213–1241 1227
Fig. 5 Tiles parallelization schema. The interactions produced by Sas well as the ones in the removal queue
RQ are handled by a dispatcher D, which checks the parallelization constraints and, if satisfied, assigns the
interactions to the λTiles workers. A collector Coutputs the communities every time τ. The dispatcher, the
workers and the collector access the graph Gvia a shared memory M
communities the node is part of, then:
The underlying idea is to operate updates on network subgraphs that are disjoint w.r.t. the
communities assigned to their nodes: this parallel decomposition of the original problem
is made possible by the constrained label propagation used to spread community member-
ship. By definition, each local perturbation affect only the communities shared by the nodes
involved in it thus allowing for parallel updates of the remaining communities. This property
also holds for the edge removal phase considering the prioritized removal queue RQ as a
streaming source. Isolating interactions among nodes of different communities allow us to
parallelize the algorithm speeding up of the computation of community evolution.
Since identifying disjoint source streams is a challenging problem, Tiles parallelization
is achieved by adopting a map-reduce approach with shared memory (see Fig. 5): to this
extent we interpose a dispatcher Dbetween the stream Sand the λTiles workers which
share the same memory space. The dispatcher collects λconsecutive updates, evaluates if
they satisfy the introduced constraints and, if so, it distributes them to the workers. If the
constraint are violated the updates are assigned to a single worker maintaining their original
order. At the end of each observation period a collector accesses the shared memory and
outputs the observed communities.
5 Experimental results
Evaluating the results provided by a community detection algorithm is a hard task, since
there is not a shared and universally accepted definition of what a community is. In literature
each approach provides its own community definition, often maximizing a specific quality
function (e.g. modularity, density, conductance, …). Even though the communities identified
by a given algorithm on a network are consistent with its community definition, it is not
guaranteed that they are able to capture the real sub-topology of the network. For this reason,
a common methodology used to assess the quality of a community detection algorithm is to
evaluate the similarity between the partition it produces with the ground truth communities
of the analyzed network.
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1228 Mach Learn (2017) 106:1213–1241
In this section, we compare Tiles to other state-of-the-art algorithms on both synthetic
and real networks with ground truth communities (Sect. 5.1). Moreover, we characterize
the communities our algorithm produces on three large-scale real-world datasets of social
interactions (Sect. 5.2), discuss the event-based community lifecycle of communities (Sect.
5.3) and, finally, analyze the impact of the ttl parameter on the node/community stability.
5.1 Evaluation on networks with ground truth communities
We compare Tiles with other static (Demon and cFinder) and dynamic (iLCD) overlapping
community discovery algorithms. In order to cope with the absence of edge removal in
the other algorithms we instantiate Tiles for an accumulative growth scenario (ttl =∞).
Demon (Coscia et al. 2012) is a bottom-up approach which exploits label-propagation to
identify communities from ego-networks.4cFinder (Palla et al. 2005) is an algorithm based
on clique percolation that searches for clique-based network structures.5iLCD (Cazabet
et al. 2010) is an algorithm for dynamic networks which re-evaluates communities at each
new interaction produced by a streaming source.6In particular, every time a new interaction
appears iLCD recomputes communities according to the path lengths between each node and
its surrounding communities.
The slightly different community definitions introduced by the chosen algorithms make
questionable a direct comparison of the outputs obtained on the same network when a ground
truth is not provided. To overcome such issue and perform the analysis in a controlled envi-
ronment we use both synthetic and real networks with ground truth communities.
5.1.1 Synthetic networks
LFR (Lancichinetti and Fortunato 2009) is the synthetic graph generators mostly used to
evaluate community discovery algorithms since it provides, along with real-world like net-
work topologies, annotated ground truth partitions. We performed a controlled experiment
by generating multiple networks varying the following LFR parameters:7
N, the network size (from 1k to 500k nodes);
C, the network density (from 0 to 0.9, steps of 0.1);
μ, the average per-node ratio between the number of edges to its communities and the
number of edges with the rest of the network (from 0 to 0.9, steps of 0.1).
Varying the values of N,Cand μ, we produced a total of 2500 different synthetic networks.
Since the LFR benchmark does not generate a timestamped stream of edges, we imposed a
random temporal ordering on the network edges in order to simulate the streaming source
Sfor Tiles and iLCD. We compared LFR ground truth communities with the communities
obtained by Tiles and the other algorithms using the Normalized Mutual Information (NMI)
score, a measure of similarity borrowed from information theory (Lancichinetti et al. 2009):
4Demon Python implementation available at:
5cFinder C implementation available at:
6iLCD Java implementation available at:
7All the algorithms were executed on a Linux 3.12.0 machine with an Intel Core i7-2600 CPU @3.4GHzx8
at 3.2GHz and 8GB of RAM.
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Mach Learn (2017) 106:1213–1241 1229
(a) (b)
Fig. 6 Comparison of the algorithms on synthetic networks with ground truth communities. aNMI versus
μ;bNMI versus network density; cruntime execution versus network size for Tiles,iLCD and Tilespthat
uses two parallel processes
where H(X)is the entropy of the random variable Xassociated to the community produced
by the algorithm, H(Y)is the entropy of the random variable Yassociated to the ground
truth community, whereas H(X,Y)is the joint entropy. NMI ranges in the interval [0,1] and
is maximized when the compared communities are identical. Figure 6compares the NMI
of the algorithms as the values of the LFR parameters vary. Varying μwe can observe that
Tiles produces communities whose NMI w.r.t. the ground truth is comparable to Demon
and cFinder, but significantly outperforms iLCD (Fig. 6a). In Fig. 6b we observe that the
NMI of the algorithms is stable till the density C<0.5, i.e. half of all the possible edges are
present in the network. As the network becomes dense (C0.5) we observe a drop in the
NMI of the algorithms. Such a high density, however, is unusual for real interaction networks
where density usually falls in the range [0.1,0.2].Fig.6c compares the execution time of
Tiles,iLCDandTiles p, an instantiation of the algorithm which exploits the composition-
ality property in order to parallelize the computation. While Tiles has an execution time
comparable to iLCD, Tiles psignificantly reduces the runtime by handling parallel updates.
5.1.2 Real networks
Moving from the results obtained on the LFR benchmark, we compared Tiles and its competi-
tor on four large-scale real-world networks. In order to do so, we adopted a novel community
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1230 Mach Learn (2017) 106:1213–1241
evaluation technique able to cope with the computational issues that arise when calculating
NMI on large community sets. Indeed, following Equation (3), given two sets of communities
Xand Ythe former identifying the community extracted by an algorithm (having size m)and
the latter representing the ground truth community set (having size n), in order to compute
NMI it is necessary to identify the best community matches with cost O(mn). Assuming
mnthe NMI computation requires O(n2)comparisons, making it not suitable for large-
scale networks. If for the synthetic networks we analyzed before the number of communities
still allowed us to compute NMI in reasonable time, this is not the case for the four real world
graphs we selected. In order to reduce the computational complexity, and thus speed up the
evaluation process, we adopted the approach proposed in Rossetti et al. (2016):8given an
algorithm community xX, (1) we label its nodes with their corresponding ground truth
community yY, then (2) we match community xwith the ground truth community with
the highest number of labels in community x. We define two measures:
Community Precision: the percentage of nodes in algorithm community xlabeled with
ground truth community y, computed as
Community Recall: the percentage of nodes in ground truth community ycovered by
algorithm community x, computed as
The two measures describe, for each pair (x,y), the overlap between algorithm community
xand ground truth community y: a perfect match is obtained when both precision and recall
are 1. We also define a quality score for the algorithm community set by computing precision
and recall on all the communities in the set and then computing their average F1-measure,
the harmonic mean of precision and recall:
F1=2precision recall
precision +recall.(6)
We applied this evaluation procedure to four large-scale real-world static networks with
ground truth communities: dblp, Youtube, Amazon and LiveJournal.9Figure 7compares the
precision and recall of communities extracted by the four algorithms on the dblp dataset. In
the density scatter plots, the upper-right corner (maximum precision and recall) identifies
the optimal community match. We observe that Tiles produces high quality communities
(high overlap with the ground truth communities) while the other algorithms overestimate the
ground truth: they generate communities that are bigger than the corresponding ground truth
communities, resulting in high recall and low precision. In Table 1we report the average F1-
measure of the algorithms on the networks. Even if Tiles is designed for a dynamic scenario,
it produces the highest F1 scores guaranteeing at the same time low standard deviation.
5.2 Characterization on large-scale real networks
Once compared Tiles with state-of-the-art approaches both in synthetic and real world data,
in this subsection we analyze the communities it produces on three large-scale real-world
8NF1 Python code available at:
9The networks are available at:
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Mach Learn (2017) 106:1213–1241 1231
Fig. 7 Community precision and community recall produced by the four algorithms on the dblp network. The
density scatter plots, compared the precision on the x-axis and the recall on the y-axis. A perfect match between
the communities identified by a given algorithm and the ground truth is observed when both precision and
recall are 1 (top-right corner of the plot). Color depth, in a gradient from yellow to red, indicates the density
of (precision, recall) points: the deeper the color of a point, the higher the density of points around it (Color
figure online)
Tab l e 1 Real world network datasets
Network Nodes Edges Coms. CC dTiles iLCD cFinder Demon
Amazon 334,863 925,872 75,149 .396 44 .78(.05) .78(.23) .77(.27) .75(.24)
Dblp 317,080 1,049,866 13,477 .632 21 .80(.09) .70(.23) .74(.24) .65(.24)
Youtube 1,134,890 2,987,624 8385 .080 20 .64(.11) .42(.20) .60(.20) .42(.10)
LiveJournal 3,997,962 34,681,189 287,512 .284 17 .73(.17) .71(.04) .32(.30) .64(.29)
For each network we indicate the number of nodes, edges and ground truth communities as well as the average
clustering coefficient, CC, and the diameter, d: moreover, the average F1 scores produced by the compared
algorithms is reported (standard deviation within brackets)
In bold the best F1 score for each network
dynamic interaction networks: a wall post network extracted from Facebook, a Chinese
micro-blogging mention network, and a nation-wide call graph extracted from mobile phone
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1232 Mach Learn (2017) 106:1213–1241
Tab l e 2 General features of the networks
Network Nodes Edges CC #Observations (τ)
CG 1,007,567 16,276,618 0.067 10 (3 days)
FB07 19,561 304,392 0.104 52 (1 week)
WEIBO 8,335,605 49,595,797 0.014 52 (1 week)
CC identifies the network clustering coefficient, τthe observation window we use in the experiments
These dynamic networks allow us to characterize the communities produced by the algo-
rithm in three slightly different scenarios: two “virtual” contexts where people share thoughts
and opinions via social media platforms, and a “real” one where people directly keep in touch
through a mobile phone device. In Table 2are reported the main statistics of the selected
5.2.1 Call graph
The call graph is extracted from a nation-wide mobile phone dataset collected by a European
carrier for billing and operational purposes. It contains date, time and coordinates of the
phone tower routing the communication for each call and text message sent by 1,007,567
anonymized users during one month. We discarded all the calls to external operators. In the
experiments we adopt as τa window of 3 days.
5.2.2 Facebook wallpost
The FB07 network is extracted from the WOSN2009 (Viswanath et al. 2009) dataset10 and
regards online interactions between users via the wall feature in the New Orleans regional
network during 2007. We adopted an observation period τof 1 week.
5.2.3 WEIBO interactions
This dataset is obtained from the 2012 WISE Challenge11: built upon the logs of the popular
Chinese micro-blog service WEIBO,12 its interactions represent mentions of users in short
messages. We selected a single year, 2012, and used an observation window of one week.
It is worth noting that any arbitrary chosen value of τdoes not affect the execution of
Tiles but only the number and frequency of community status observation. The τthreshold
is introduced with the purpose of simplifying the analysis of results reducing the number
of community observations. Note that in order to get as output the complete history of
community updates (an observation for each local perturbation) it is sufficient to set τequal
to the clock of the streaming source.
We analyzed four aspects of the communities produced by Tiles: (1) the distribution of
community size; (2) the distribution of community overlap; (3) the distribution of communi-
ties’ average clustering coefficient; (4) the transition time of nodes from the periphery to the
core of communities. The size of Tiles communities follows a heavy tail distribution for all
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Mach Learn (2017) 106:1213–1241 1233
(a) (b)
(c) (d)
(e) (f)
Fig. 8 (First row) Community size distribution fitted powerlaw for CG (a), FB07 (b)andWEIBO(c).(Second
row) Node overlap distribution fitted powerlaw for CG (d), FB07 (e)andWEIBO(f)
networks (Fig. 8a, b, c). This means that the vast majority of communities have few nodes
while a small but significant portion of them have several thousands nodes. Such a great het-
erogeneity also characterizes the community overlap, i.e. how many different communities a
node belongs to (Fig. 8d, e, f). The majority of nodes belong to just one or two communities,
while some nodes belong to thousands different communities. Figure 9shows the average
clustering coefficient of communities computed over the core nodes. Communities maintain
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1234 Mach Learn (2017) 106:1213–1241
(a) (b)
Fig. 9 Distribution of average clustering coefficient per community for CG (a), FB07 (b)andWEIBO(c).
Each color identify a ttl value
high average clustering coefficients as time goes by, with minimum values of 0.6 for FB07
and WEIBO networks and 0.8 for CG. These values are significantly higher than the overall
clustering coefficients of the networks (see Table 2) highlighting that Tiles is capable of iden-
tifying dense network sub-structures. Moreover, increasing the ttl value causes a decrease
in the average CC, with the exception of WEIBO for which we can observe a low drift (Fig.
9d, e, f). An explanation for this trend is that, when the interactions are persistent in time,
communities grow at the expense of their internal cohesion causing a decrease of internal
clustering coefficient.
Tiles’ definition also affects the overall node coverage of the identified communities,
i.e. how many nodes are included into communities. This property is hence strictly related
to the clustering coefficient of the analyzed network: the greater the clustering coefficient
the higher is the nodes coverage. This tendency is depicted in Fig. 10a,b,cwherewe
report, for CG, FB07 and WEIBO, how the ratio of “core” nodes, “periphery” nodes and
the sum of the two (total nodes coverage) change in time during the period of observa-
tion. FB07 has a clustering coefficient of 0.104 showing a high coverage: the 80% of nodes
are included in some communities. In contrast, CG and WEIBO reach a coverage rang-
ing between 40 and 50 %, due to their low overall clustering coefficient (0.067 and 0.014,
A peculiarity of Tiles is the concept of community periphery. As discussed in Sec-
tion 4peripheral nodes are not involved in triangles with other nodes of the community.
Every node first joins a community as peripheral node then it becomes a core one once
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Mach Learn (2017) 106:1213–1241 1235
(a) (b)
(c) (d)
(e) (f)
Fig. 10 (First row) Tiles communities nodes coverage; (Second row) Distribution of transition time from
periphery to core (transitions in) and viceversa (transition out) as well as ratio of nodes that move from the
periphery to the core across consecutive observations (periphery impact). aCG-coverage, bFB07-coverage,
cWEIBO-coverage, dCG, eFB07, fWEIBO
it is involved within a triangle with other core nodes. We found that the expected time of
transition from the periphery to the core of a community is generally short (Fig. 10d, e, f,
Transitions In): in CG 40% of nodes become core nodes in just 3 days; in FB07 15 % of
nodes perform the transition during the first week; in WEIBO almost 60 % of transitions
occur within a single week. Moreover, the transitions of nodes from the core to the periphery
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1236 Mach Learn (2017) 106:1213–1241
Fig. 11 Example of community lifecycle extracted from WEIBO. Each community is represented by a circle
and identified by a number. Events are identified by the relative letter: (B) birth, (M) merge, (A) absorption,
(S) split, (D) death. Merged communities and residual of splits are highlighted with thicker lines
(Fig. 10d, e, f, Transitions Out) follow distributions similar to the ones observed for the
reverse path. However, if we do not consider the distributions shapes but the total number
of both events an interesting pattern emerges: the number of nodes that are “attracted” by
the core of communities are between 2 and 900 times more of than the nodes that follow
the opposite route (263,483 to 8415 in FB07, 680,932 to 420,530 in CG and 6,373,316
to 7070 in WEIBO). This peculiarity highlights that the community cores are able to pro-
vide meaningful—and stable—boundaries around nodes that frequently interact with each
We also investigated how many nodes perform the transition from periphery to core
across consecutive community observations: we asked ourself, given two observation of a
community C, what is the ratio of core nodes in Cat t+that where in the peripheral nodes
at time t?InFig.10d, e, f dashed line, we observe that this ratio has values between the 30 %
and 50 % of the nodes for CG and FB07, and around 70 % for WEIBO. This means that in
all the networks almost the half of the peripheral nodes become core nodes in the subsequent
time window.
5.3 Event-based community lifecycle and time to leave analysis
Several works on evolutionary community discovery focus their attention on the identification
and analysis of the events that regulates community life-cycles—birth, merge, split and
death of communities. In this section we show how Tiles allows us to easily capture these
events by observing step by step the network evolution and tracking the perturbations of the
communities. In order to perform an analysis of community life-cycles we identify five main
Birth (B): the community first appearance, i.e. the rising of the first set of core nodes of
the community;
Merge/Absorption: two or more communities merge when their core nodes completely
overlap: we define as Absorbed (A) the communities which collide with an existing one,
we define as Merged (M) the already existing community;
Split (S): a community splits in one or more sub-communities as consequence of the
edge removal phase;
Death (D): a community dies when its core node set becomes empty.
In Fig. 11 is reported an example of community life-cycle extracted from the WEIBO network.
Figure 12 shows the event trends, i.e. the number of events of a given type as time goes by, for
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Mach Learn (2017) 106:1213–1241 1237
(a) (b)
Fig. 12 The number of community events as time goes by in the WEIBO network for aone week removal
scenario and bone month removal scenario. Each line represents a community event (birth B, merge M,
absorption A, split S, death D)
the WEIBO network when the ttl is set to one week (a) and one month (b).13 We can observe
that all the compared trends follow similar patterns regardless the value of ttl, expressing
only a slight increase of merge events and decrease of split events when ttl is set to one
We analyzed how the choice of ttl affects the communities characteristics by executing
Tiles with different ttl values on CG (1 day, 3 days, +), FB07 (1 week, 2 weeks, 3weeks)
and WEIBO (1 week, 2 weeks and 1 month). We have shown that ttl affects the trends of
community life-cycle events: now our aim is to understand the degree of stability it induces
on Tiles communities at a micro level. To do so we analyze the impact of ttl on the rate nodes
join and leave communities. Figure 13 shows two series of plots: (1) on the top, the trend
values for rate of joins and leaves; (2) on the bottom, how this trend relates on average to the
stability of communities (how many communities per observation are affected by at least a
join/leave action). We observe that, as ttl increases, nodes and communities stabilize quickly
and leave actions emerge less frequently. The reduction of leave actions depends on the ttl:
by extending the time to live an interaction is more likely to renew than to expire. On the other
hand, Fig. 14 shows the community average life (the number of weeks/days from its rising
to its disappearance) w.r.t. its average size. A correlation between the two measures clearly
emerges: in FB07 (average Pearson correlation ρ=.88, standard deviation σ=.03) and CG
(ρ=.4, σ=.11) the bigger a community is the longer it lives regardless the value of ttl.In
contrast, in WEIBO we observe a negative correlation (ρ=−.52, σ=.13) between size and
average community life. Moreover, increasing ttl the expected life and size of communities
tends to grow reaching their maximum when the removal phase is avoided (ttl →+).
This observation is reinforced by the average clustering coefficient trend shown in Fig. 9:
lower ttl values produce more compact community structures.
Our experiments show that interactions’ ttls deeply affect the outcome of the algorithm.
In particular:
higher ttl values produce bigger communities and foster the stabilization of the node
–lowerttl values produce smaller, denser, and often more unstable communities.
13 FB07 and CG show similar behaviors.
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1238 Mach Learn (2017) 106:1213–1241
(c) (d)
(e) (f)
Fig. 13 Evolution of community memberships as time goes by. acscenarios for low ttl: CG one day, FB07
one week, WEIBO one week; dfscenario for high ttl: CG and FB07 no removal, WEIBO one month
We can argue that reasonable values for this threshold are the ones which lead to a stability
rate trend (for both nodes and communities) that overcome the join/leave ones. When this
condition is satisfied, Tiles is able to extract communities having stable life-cycles (e.g.,
they do not appear and fall apart quickly). However, the choice of ttl is obviously context
dependent and it reasonable to assume that different phenomena can be characterized by
different interactions persistence.
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Mach Learn (2017) 106:1213–1241 1239
(a) (b)
Fig. 14 Community size versus average community life for aCG, bFB07 and cWEIBO networks. Each
marker identifies a different ttl value
6 Conclusion and future works
In this paper we proposed Tiles, a community discovery algorithm which tracks the evo-
lution of overlapping communities in dynamic social networks. It follows a “domino”
approach: each new interaction determines the re-evaluation of community memberships for
the endpoints and their neighborhoods. Tiles defines two types of community memberships:
peripheral membership and core membership, the latter indicating nodes involved in at least
a triangle within the community. An interesting property of Tiles is compositionality, which
allows for algorithm parallelization, thus speeding up the computation of the communities.
Other interesting characteristics emerged by the application of the algorithm on large-scale
real-world dynamic networks, such as the skewed distribution of Tiles community size and
their high average clustering coefficient. Compared with other community detection algo-
rithms both on synthetic and real networks, Tiles shows better execution times and a higher
correspondence with the ground truth communities. Moreover we shown how our approach
enables the identification of the main events regulating the community life-cycle (i.e., birth,
merge, split and death).
Many lines of research remains open for future works, such as identifying a more complex
and precise way to manage the removal phase: indeed, one limit of the current approach is the
needs of defining explicitly a time to leave threshold that is the same for all the interactions
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1240 Mach Learn (2017) 106:1213–1241
among the nodes within the social network. To overcome this issue we plan to define a
data driven approach able to dynamically provide an estimate of the expected persistence
for each single interaction. Moreover, the mechanisms which regulate the node transitions
from the periphery to the core of a community is another interesting aspect we propose
to investigate: once fully understood it can be exploited as predictive information in a link
prediction scenario, or used to explore how the transition of nodes from periphery to core
can affect the spreading of information over the network.
Acknowledgements This work is partially Funded by the European Community’s H2020 Program under the
funding scheme “FETPROACT-1-2014: Global Systems Science (GSS)”, Grant agreement #641191 CIM-
PLEX “Bringing CItizens, Models and Data together in Participatory, Interactive SociaL EXploratories”, This work is supported by the European Community’s H2020 Program
under the scheme “INFRAIA-1-2014-2015: Research Infrastructures”, Grant agreement #654024 “SoBig-
Data: Social Mining & Big Data Ecosystem”,
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Interna-
tional License (, which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons license, and indicate if changes were made.
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... Several representations of temporal networks exist, each associated to different algorithms and methods, for example, as a sequence of static graphs representing time windows over which the activity is aggregated (6), as contact sequences when events are instantaneous in continuous time, or as interval graphs (7) or link streams (8,9) in continuous time with events that may have a duration. The study of the dynamics and structure of time-dependent networks has attracted many contributions from several fields such as sociology (10,11,12), computer science (8,13,14,15,16,17), epidemiology (18,19), mathematics, and network science (6,20,21,22,23,24,25) (references are not exhaustive). ...
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Traditional approaches to user engagement analysis focus on individual users. In this paper we address user engagement analysis at the level of groups of users (social communities). From the entire Skype social network we extract communities by means of representative community detection methods each one providing node partitions having their own peculiarities. We then examine user engagement in the extracted communities putting into evidence clear relations between topological and geographic features of communities and their mean user engagement. In particular we show that user engagement can be to a great extent predicted from such features. Moreover, from the analysis it clearly emerges that the choice of community definition and granularity deeply affect the predictive performance.
Evaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating the adherence of a community partition to the ground truth: our methodology provides more information than the state-of-the-art ones and is fast to compute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.
Conference Paper
Online social networks are often defined by considering interactions over large time intervals, e.g., consider pairs of individuals who have called each other at least once in a mobilie-operator network, or users who have made a conversation in a social-media site. Although such a definition can be valuable in many graph-mining tasks, it suffers from a severe limitation: it neglects the precise time that the interaction between network nodes occurs. In this paper we study interaction networks, where one considers not only the social-network topology, but also the exact time that nodes interact. In an interaction network an edge is associated with a time stamp, and multiple edges may occur for the same pair of nodes. Consequently, interaction networks offer a more fine-grained representation that can be used to reveal otherwise hidden dynamic phenomena in the network. We consider the problem of discovering communities in interaction networks, which are dense and whose edges occur in short time intervals. Such communities represent groups of individuals who interact with each other in some specific time instances, for example, a group of employees who work on a project and whose interaction intensifies before certain project milestones.We prove that the problem we define is NP-hard, and we provide effective algorithms by adapting techniques used to find dense subgraphs. We perform extensive evaluation of the proposed methods on synthetic and real datasets, which demonstrates the validity of our concepts and the good performance of our algorithms.
Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mech-anisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.