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Spatial Pattern and Evolution of Global Innovation Network from 2000 to 2019: Global Patent Dataset Perspective

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In the era of the knowledge economy, the improvement of national innovation systems is playing a significant role in the global entrepreneurship ecosystem. Entrepreneurs are accelerating international intellectual property applications to be competitive. What remains to be explored is the evolution of international intellectual property network in the globe. With the application of social network analysis and intellectual property application database, the global innovation network structure from 2000 to 2019 is explored. Results showed that (1) in the period 2000–2019, the global innovation network has been expanding rapidly from a sparse network to a dense and complex one. (2) Patent application is unevenly distributed in the globe. Countries such as the US, China, and Canada have been the top countries flowing in, while Japan, Korea, EU, and Switzerland have been the main countries flowing out. (3) Global innovation network shows an obvious “core-periphery” pattern. The distribution pattern presents a quadrilateral structure with the four core regions of “US, Japan, EU, and China” as the apex. This analysis contributes to the visualization of the global layout of intellectual property and the evolution trend by analyzing intellectual property application networks. This can provide important experience reference for enterprises to study the global entrepreneurship ecosystem.
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Research Article
Spatial Pattern and Evolution of Global Innovation Network from
2000 to 2019: Global Patent Dataset Perspective
Yuna Di,
1
Yi Zhou,
2
Lu Zhang ,
1
Galuh Syahbana Indraprahasta ,
3
and Jinjin Cao
2
1
School of International Economics and Management, Beijing Technology and Business University, Beijing 100048, China
2
School of Economics, Beijing Technology and Business University, Beijing 100048, China
3
Research Center for Population, National Research and Innovation Agency Indonesia (BRIN), Jakarta, Indonesia
Correspondence should be addressed to Lu Zhang; zhanglu113@mails.ucas.ac.cn
Received 9 February 2022; Revised 3 May 2022; Accepted 18 May 2022; Published 10 June 2022
Academic Editor: Haitao Ma
Copyright ©2022 Yuna Di et al. is is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In the era of the knowledge economy, the improvement of national innovation systems is playing a significant role in the global
entrepreneurship ecosystem. Entrepreneurs are accelerating international intellectual property applications to be competitive.
What remains to be explored is the evolution of international intellectual property network in the globe. With the application of
social network analysis and intellectual property application database, the global innovation network structure from 2000 to 2019
is explored. Results showed that (1) in the period 2000–2019, the global innovation network has been expanding rapidly from a
sparse network to a dense and complex one. (2) Patent application is unevenly distributed in the globe. Countries such as the US,
China, and Canada have been the top countries flowing in, while Japan, Korea, EU, and Switzerland have been the main countries
flowing out. (3) Global innovation network shows an obvious “core-periphery” pattern. e distribution pattern presents a
quadrilateral structure with the four core regions of “US, Japan, EU, and China” as the apex. is analysis contributes to the
visualization of the global layout of intellectual property and the evolution trend by analyzing intellectual property application
networks. is can provide important experience reference for enterprises to study the global entrepreneurship ecosystem.
1. Introduction
Innovation is becoming the key driver for the survival and
growth of firms in emerging and developed countries. With
the continuous development of technology and the deep
division of the global value chain, the development strategy
of enterprises is entering an era in which growth is driven by
knowledge innovation. For nations, innovation is far more
important than land, physical capital, or labor in a
knowledge-based economy, and is the dominating factor
affecting different economic growth and development. As a
result, the effective management of knowledge and inno-
vation has gained increasing interest in the public debate,
calling upon the contribution of scholars and practitioners
[1]. One of the important views is that innovations never
occur in isolation but on the contrary generated by networks
of interacting actors (e.g., organisations, multinational en-
terprises, and individuals) [2]. For instance, multinational
enterprises engage in overseas innovation for adapting
products to meet the unique requirements in foreign
markets [3]. us, inbound and outbound flows of
knowledge activated by the stakeholders’ interactions within
a country make today’s innovation ecosystem is highly
globally interlinked [4].
In the era of the knowledge economy, intellectual
property has become the core innovation factor, infiltrating
into all fields of international trade. e identification of the
main actors involved in regions has been revised across the
years by, most recently, including the emerging markets
[5, 6]. e concept of an innovation network was first
proposed by Ernst [7] after he studied the internal rela-
tionship between regional production and global produc-
tion. Since then, increasing studies have started related
research, such as topological properties [8–10], spatial
pattern [11], evolution process [12, 13], and mechanisms.
e previous studies generally revealed the characteristics of
Hindawi
Complexity
Volume 2022, Article ID 5912696, 11 pages
https://doi.org/10.1155/2022/5912696
innovation network, such as scale-free [8, 9], hierarchical
[8, 9], and spatial agglomeration [12, 13]. It is also widely
verified that cognitive proximity, social proximity, organi-
zational proximity, institutional proximity, and geographic
proximity are important factors affecting the evolution of
innovation networks [14–18]. However, in view of the re-
search scale, these studies have been conducted on either
country or region scale because of the data availability
[19, 20]. As the importance of interactions between different
national innovation systems, the question arises that how
nations perform in the global innovation network. Under-
standing knowledge network from a global view and
monitoring the network evolution change in a global scale is
necessary for enhancing innovation capability.
e intellectual property is an important driving force to
encourage entrepreneurship, and an important means to
maintain entrepreneurial achievements and promote the
sustainable development of enterprises [21]. e intellectual
property network has gradually become the main way to study
regional knowledge spillover, innovation and technology
diffusion, regional development path, innovation cluster, and
other practical problems. e improvement of international
intellectual property system, which is led by World Intellectual
Property Organization (WIPO), is playing a significant role in
the construction of global entrepreneurship ecosystem. WIPO
provides us with a novel approach to obtaining innovation
flows in global scale. us, the aim of this study is to explore
how the structure of globalization of technology via intel-
lectual property networks has changed longitudinally. e
identification of the main countries in the global innovation
network can not only describe the innovation performance
and development strategy of each country but also can help to
figure out the trend of global economy.
e rest content is divided into four sections. e first
section briefly describes the data collection and method applied
in this analysis. e results of properties, “core-periphery”
pattern of global innovation network during 20002019 will be
presented in the third section. e fourth section discusses the
main findings and potential limitations outlined in this section.
Following this section, an overview of potential avenues for
further research is presented. is analysis will contribute to
current studies by visualizing the global layout of intellectual
property, which provides references for enterprises to study the
global entrepreneurship ecosystem.
2. Methodology
2.1. Data Source and Network Generation. In this study, we
use a comprehensive dataset from WIPO to investigate the
structure and dynamics of global innovation network from
2000 to 2019. Innovation has been the primary driving force
for development in the past decades. In the twenty-first
century, especially, globalization, information and intelli-
gent development, which are inseparable from technological
innovation, have provided large support for the world
economy. In order to explore the evolution of global in-
novation since the 21
th
century, the year 2000 is chosen as
the starting year while the year 2019 is chosen as the ending
year due to the emergence of COVID-19 in 2020.
WIPO, which manages the international application
system and the global intellectual property system, is an
intellectual property management agency affiliated with the
United Nations. Compared with other intellectual property
databases, it has a long history (since 1967) with a large
volume of data (more than 55 million patent records) and a
large number of member countries (193 member countries).
It provides free intellectual property information as well as
patents from the African Regional Intellectual Property
Organization (ARIPO), the Eurasian Patent Organization
(EAPO), and the European Patent Office (EPO) and regional
patent office’s records. Furthermore, the WIPO dataset can
provide the information on filing office and application’s
origin which is basic for constructing networks in our
analysis. erefore, the dataset of the WIPO Intellectual
Property Statistics Data Center (https://www3.wipo.int/
ipstats) is applied in this analysis. Based on the availabil-
ity and reliability of data, this analysis selects 203 countries
and regions as the research objects. e gross domestic
product (GDP) and patent applications of these countries
and regions account for more than 90% of the global value,
which can represent the basic situation of the global in-
novation cooperation network.
In the process of data collection, this analysis selects the
“total number of patent applications (direct application and
entry into the PCT national phase)” instead of granted
applications. Due to the complicated process of application
and differences between countries, the approval process of
one patent might take years. For this reason, the number of
granted applications cannot reflect the latest technological
development and changes in the field of innovation. e
report type selection is counted by the filing office and the
origin of the applicant. In order to analyze the evolution of
the global innovation cooperation network, data from 2000
to 2019 are collected and applied in this analysis. Besides,
there are some missing data in individual countries in some
years. To minimize the impact of missing data, the pro-
cessing method of this analysis is (1) to supplement the
missing data with interpolation; (2) to average all the data
every five years. In addition, this analysis merges the
countries that joined the European Union (EU, hereafter)
that year into the EU. Finally, this analysis constructs a
patent data matrix for four periods: 2000–2004, 2005–2009,
2010–2014, and 2015–2019. According to the selected
countries identified above, there were 185 nodes in the global
innovation network in 2000–2004, 175 nodes in 2005–2009,
173 nodes in 2010–2014, and 172 nodes in 2015–2019. e
change in the number of nodes is due to the accession of
some countries to the EU during the study period. us,
directed adjacent matrices of 185 185, 175 175, 173 173,
and 172 172 were constructed based on the strength of
patent edges (Figure 1). e values in the matrix represent
the number of patents between the two countries, and the
matrix is directed.
2.2. Indicators of Network Analysis. By constructing a
weighted network, we explore the direction and strength of
the connection between countries. is analysis adopts a
2Complexity
directed weighted network, which can reasonably evaluate
the spatial structure characteristics of innovation network.
2.2.1. Key Indicators. is analysis aims to analyze the
network structure and evolution of the global intellectual
property network (GIPN) from network scale, small-world
property, scale-free property, centrality properties’ views. In
general, G (V, E)stands for a directed network, Vis the set
of nodes while Eis the set of edges. Density and network
diameter are used to indicate the scale of the GIPN. e
value of density refers to the ratio of the number of actual
network relationships to the maximum number of possible
relationships. e network diameter is the longest path of
any two nodes in the entire network. e specific calculation
formulas are
D2M
N(N1),
Rmaxvi,vjεVr vi, vj
􏼐 􏼑􏼐 􏼑,
(1)
where Drefers to the network density, Rrefers to the
network diameter, Mrefers to the number of relationships
existing in the actual network, and Nrepresents the number
of nodes in the network. vi,vjV,r(vi, vj)represents the
longest path between nodes iand j.
e average clustering coefficient and average path
length are used to analyze small-world properties of the
GIPN. e average cluster coefficient is the mean of the
clustering coefficient of all nodes. e cluster coefficient of a
node is the density of its open neighborhood [22]. e
neighborhood (Ni) of node viis the set of its adjacent nodes.
Niis defined as
Nivj:eij ϵEeji ϵE
􏽮 􏽯.(2)
Cluster coefficient is calculated by means of the following
formula:
Ciejk:vj, vkϵNi, ejk ϵE
􏽮 􏽯
􏼌􏼌􏼌􏼌􏼌􏼌􏼌􏼌􏼌􏼌
N(N1),(3)
where Cirefers to the cluster coefficient of vi. Average
clustering coefficient (C) is calculated as
C1
N􏽘
n
i1
Ci.(4)
e average path length is the average number of edges
along the shortest path for all possible pairs of nodes.
L1
N(N1)􏽘
i,j
d vi, vj
􏼐 􏼑,(5)
where Lrefers to the average path length and d(vi, vj)
represents the shortest path between nodes iand j.
Centrality measurements are used to identify nodes that
are most critical and central in networks. Among which,
degree, betweenness, and closeness centralities are the most
widely used indexes. Degree centrality is calculated by means
of the following formula:
DCi􏽘
N
j1
wij.(6)
e degree centrality can be calculated according to
weighted network data or binary network data. In this
analysis, weighted data are used to calculate the degree
centrality of each node. wij is the number of patents between
country iand j.
Closeness centrality refers to the mean geodesic path
between nodes in networks. Here, the geodesic path is de-
fined as the number of edges traversed from node ito j. us,
a node with a high closeness centrality indicates a short
communication edge to other nodes in networks. In general,
geodesic paths are not unique, as there can be several paths
between two given nodes with the same shortest length.
However, at least one geodesic path always exists between
any two nodes in the same connected component of a
network. e mean geodesic distance between iand all other
nodes in the network is given by
gi1
N􏽘
n
j1
cij.(7)
Here, cij refers to the number of edges traversed from
node ito j. en, the closeness centrality CCiof node iis
defined as follows:
CCi1
gi
N
􏽐n
j1cij
.(8)
Another notion of centrality is betweenness centrality,
which measures the number of short paths between nodes
while they pass through a given node. us, we define
vi(s, t) 1,if ilies on the geodesic path from sto t
0,otherwise,
􏼠 􏼡.(9)
Country-C
Country-A Country-B
Apply patents in country B
Apply patents in country A
Apply patents in country C
Apply patents in country A
Apply patents in country B
Apply patents in country C
Figure 1: Construction of innovation network with patent.
Complexity 3
en, the betweenness centrality BCiof node iis
BCi􏽘
s,tN
vi(s, t).(10)
DCiand CCianalyze the properties of nodes. Graph
centrality is used when the focus is on the whole network.
is analysis uses graph degree centrality and graph be-
tweenness centrality to analyze the centrality trend of the
whole network [23]. e specific calculation formulas are
DC 􏽐N
i1DCmax DCi
􏼁
N23N+2,
BC 􏽐N
i1BCmax BCi
􏼁
N34N2+5N2,
(11)
where DC and BC refer to the graph degree centrality and
graph betweenness centrality, and DCmax and BCmax repre-
sent the maximum value of DCiand BCi.2.2.2. In-Out Flow
e in-out flow is calculated based on out-flow and in-
flow. e out-flow is defined as
Oi􏽘
n
ij
Sij,(12)
where Sij refers to the number of patents flowing from
country ito country j.Oirefers to the number of patents
flowing out of country i. e in-flow is defined as
Ii􏽘
n
ij
Sij
,(13)
where Sij
refers to the number of patents filed by country iin
country j.Iirefers to the total number of patents filed by
country i. en, we compare the out-flow and in-flow, which
is referred as in-out flow.
In out flowiIiOi.(14)
ere are two possible results: (i) In out flowi>0,
which presents country ihas more in-flow than out-flow,
indicating the number of patents flowing into country iis
higher than the number of patents flowing out. (ii)
In out flowi<0, which presents country ihas more in-flow
than out-flow, indicating that the number of patents flowing
out from country iis higher than the number of patents
flowing in.
2.3. Coreness. ere is often a core edge structure in the
network, so the coreness is introduced to quantitatively
study the status of each node in the network, and have a
quantitative understanding of where the node is (core,
semicore, and periphery). When calculating the coreness,
each node needs to be given a coreness ci. e coreness is
calculated by the following steps:
δij c.cT.(15)
δij is the pattern matrix of the network data matrix Wij
to be analyzed. cis the eigenvector, and cTis its transpose
vector. e core-periphery analysis method finds the ei-
genvector c, which can make the correlation coefficient
maximum between the actual matrix Wij and the pattern
matrix δij. e element ciin eigenvector cis the coreness of
each node in the network. is indicator is achieved by the
Ucinet platform.
In order to construct a continuous core-periphery
model, nodes in the GIPN are divided into the following four
layers: nodes with a coreness greater than 0.2 belong to core
layer; nodes with a core degree between 0.01 and 0.2 are
semicore countries, nodes with a coreness between 0 and
0.01 are semicore countries, and countries with zero core-
ness are considered as peripheral nodes.
3. Results
3.1. Key Indicators. Table 1 presents the summary statistics
of complexity of the GIPN from 2000 to 2019. e following
observations can be gained.
(1) e density and the number of edges increase, and
the network becomes denser. From 2000 to 2014,
the scale of the GIPN expanded rapidly, and the
number of nodes in the network declined. is is
due to the increasing number of EU countries in the
sample. At the same time, the number of edges
expanded rapidly, from 1772 in 20002004 to 3070
in 20152019. e network density increased rap-
idly from 0.0521 to 0.1044, indicating that the global
urban innovation network has gradually grown
from a sparse network to a dense and complex
network.
(2) In view of the centrality indicators, the weighted
degree centrality increased from 8.14 to 15.08. e
average intermediary centrality of the GIPN has
gradually increased, indicating that some countries
have gradually increased their ability to control and
deliver innovation. Besides, closeness centrality in-
creased from 2.87 to 7.23, indicating that the rela-
tionship between countries and the core countries of
the innovation network is getting closer.
(3) Both degree centrality and betweenness centrality
have large Gini coefficients and coefficients of var-
iation, and the nodes in the network are very po-
larized. e Gini coefficient of degree centrality is
relatively large, remaining above 0.88, indicating that
the GIPN exhibits a strong discrete trend and ex-
tremely unbalanced characteristics.
(4) In view of the evolution over the years, the coefficient
of variation of degree and betweenness centrality
shows a downward trend, indicating that the discrete
trend of the GIPN has declined.
3.2. Global Distribution. In this section, the topological
relationship is converted to spatial connections with the
application of the ArcGIS platform. e difference between
in-flow and out-flow in 2000–2019 is visualized in Figure 2,
and three observations were gained.
4Complexity
First, in view of the overall distribution pattern of the
GIPN, countries that were most deeply embedded in GIPN
include the US, China, Korea, Japan, EU, Switzerland, and
Canada, as shown in Figure 2. Specifically, the US, China,
and Canada have been the top countries with large in-flow
patent and small out-flow patents during 2000–2004. Since
then, China and the US have jointly occupied important
positions in the GIPN. Besides, Japan, Korea, EU, and
Switzerland have been the top countries with large out-flow
patents and small in-flow patents. Second, the transferring
direction has been concentrated among these top cities. For
instance, the in-flow and out-flow patents among the USA,
Japan, China, and Korea occupied more than 60% of the
total. Besides, the transferring direction of patented tech-
nology is mainly east-west in the northern hemisphere.
ird, increasing in-flows in Asian countries can be ob-
served. ese Asian countries include Viet Nam, ailand,
Malaysia, Singapore, and Indonesia.
3.3. In-Out Flow of the Global Intellectual Property Network.
For our interests in the positions of a country, the countries’
position is overlaid by differencing in-flow and out-flow. e
analysis of ranking countries in the GIPN was conducted in
the last 20 years periods from 2000 to 2019. In view of the in-
flow, our results point to the evolution of countries’
positions in the GIPN (Figure 3). e first point to make here
is that the primary three countries, including the US, Japan,
and China (Figure 3, dark lines), have remained the top three
positions in the past years. Specifically, the total in-flow in
the three countries share more than 52% of the total volume.
Moreover, the concentration of in-flow in the three coun-
tries has been increasing. Specifically, the in-flow volume
accounts for 52.2% during 2000–2014 while the percentage
has increased to 63.6% during 2015–2019. is indicates that
the three countries possess high position relative to their
economic and innovation potential in the GIPN. e second
point is that some countries have raised their position
largely. ese countries include Korea, India, and Indonesia
reflecting the trend from the low rank to the high rank in the
GIPN. e third point is that some countries, including
Canada, EU, and Brazil, rank from higher to lower positions.
In view of the out-flow, our results point to the evolution
of countries’ positions in the GIPN (Figure 4). e first point
to make here is that the primary three countries, including
EU, Japan, and the US, have remained in the top three
positions in the past years. Specifically, the total in-flow in
the three countries shares more than 49% of the total vol-
ume. e concentration of the out-flow in the three
countries has been decreasing. Specifically, the out-flow
volume account for 51.7% during 2000–2014 while the
percentage has decreased to 49.6% during 2015–2019. is
Table 1: Complexity statistics of global intellectual property network from 2000 to 2019.
Statistical
characterstics Index 2000–2004 2005–2009 2010–2014 2015–2019
Network scale
Number of nodes 185 175 173 172
Number of edges 1772 1791 2650 3070
Density 0.0521 0.0588 0.0891 0.1044
Diameter 4 3 3 4
Small-word
property
Average clustering
coefficient 0.76 0.785 0.752 0.757
Average path length 1.918 1.828 1.823 1.804
Power law fitting of
degree centrality y1618.2X1.313 y1415.3x1.271 y1965.8x1.246 y1972.7x1.216
Scale-free
property
R2 0.7984 0.7871 0.7405 0.7262
Exponential fitting of
degree centrality y88.082e0.031x y 90.871e0.032x y 125.85e0.029x y 138.6e0.029x
R2 0.9895 0.9847 0.983 0.9807
Degree centrality
Average degree
centrality 8.143 9.051 13.503 15.028
Graph degree
centrality 0.539 0.577 0.61 0.641
Coefficient of variation 1.487 1.443 1.19 1.135
Gini coefficient 0.961 0.96 0.955 0.954
Betweenness
centrality
Average betweenness
centrality 0.369 0.379 0.473 0.458
Graph betweenness
centrality 0.143 0.144 0.138 0.157
Coefficient of variation 4.615 4.403 3.677 3.852
Gini coefficient 0.887 0.941 0.916 0.91
Closeness
centrality
Average closeness
centrality 2.872 2.049 7.213 7.23
Coefficient of variation 0.0063 0.0066 0.0177 0.0177
Gini coefficient 0.003 0.004 0.01 0.01
Complexity 5
indicates that the three countries possess high positions
relative to their economic and innovation potential in the
GIPN. e second point is that some countries have raised
their position largely. ese countries include China, India,
Singapore, and Kazakhstan, reflecting the trend from the low
rank to the high rank in the GIPN. e third point is that
some countries, including Switzerland, Canada, and New
Zealand, rank from higher to lower positions.
2000-2004 In-out flow
Legend
< –20000
< - 10000
> 20000
> 10000
–5000 - –20000
–10000 - –3000
3000 - 10000
–5000 - 0
0 - 5000
5000 - 20000
Legend
2005-2009 In-out flow
< –20000
< - 10000
> 20000
> 10000
–5000 - –20000
–10000 - –3000
3000 - 10000
–5000 - 0
0 - 5000
5000 - 20000
Legend
2010-2014 In-out flow
< –20000
< - 10000
> 20000
> 10000
–5000 - –20000
–10000 - –3000
3000 - 10000
–5000 - 0
0 - 5000
5000 - 20000
Legend
2015-2019 In-out flow
< –20000
< - 10000
> 20000
> 10000
–5000 - –20000
–10000 - –3000
3000 - 10000
–5000 - 0
0 - 5000
5000 - 20000
Figure 2: In-out flows of countries in 2000–2019.
6Complexity
3.4. Core and Periphery of the Global Intellectual Property
Network. e centrality is an important indicator to mea-
sure the degree of centralization of the entire network. From
2000 to 2019, the degree centralization of the GIPN was
basically maintained at about 0.6, and the closeness cen-
tralization was basically maintained at about 0.65, indicating
Up
China
US
Japan
China
Canada
Korea
EU
Australia
Brazil
Mexico
India
Russia
Singapore
New Zealand
Malaysia
Israel
South Africa
ailand
Indonesia
Philippines
Viet Nam
2000-2004 2005-2009 2010-2014 2015-2019
Korea
India
Indonesia
ailand
South Africa
Viet Nam
Philippines
US
Australia
Russia
Singapore
Japan
Canada
EU
Brazil
Mexico
Malaysia
Israel
New Zealand
Unchanged
Down
Figure 3: Positions of countries in in-flow.
Up
US
China
India
Singapore
Saudi Arabia
Brazil
Turkey
Malaysia
EU
Korea
Israel
Russia
Mexico
Japan
Switzerland
Canada
Australia
Norway
New Zealand
South Africa
Unchanged
Down
2000-2004
EU
Japan
US
Korea
Switzerland
Canada
Australia
Israel
China
Norway
India
New Zealand
Russia
Singapore
South Africa
Brazil
Mexico
Malaysia
Turkey
Saudi Arabia
2005-2009 2010-2014 2015-2019
Figure 4: Positions of countries in-out flow.
Complexity 7
that the entire network has a relatively obvious direction,
indicating a significant core-periphery structure with hier-
archies in the GIPN (Figure 5). In sum, the following three
observations can be gained.
(1) In the core layer, the GIPN gradually developed from
a triangular structure to a quadrilateral pattern.
Specifically, the coreness of the four periods from
2006 to 2010 has always been higher than 0.8, be-
coming the absolute core of the network. EU and
Japan have far lower patent transfers than the US. As
a result, the core countries from 2000 to 2009 are the
US, Japan, and the EU. China has performed well,
and its core degree has always increased, from 0.105
in 2000–2004 to 0.262 in 2015–2019, further nar-
rowing the gap with the EU. As a result, China ranks
among the core countries of the innovation network.
(2) In the semicore (second) layer, it has maintained a
relatively stable state. Specifically, Australia, Brazil,
Canada, China, India, Israel, Mexico, Korea, Russia,
Singapore, and Switzerland have stayed in the
semicore layer.
(3) e innovation network presents an obvious phe-
nomenon of cooperation aggregation-technical co-
operation within core countries, between core and
semicore countries, is intensive, while the partici-
pation of peripheral countries in the GIPN is limited.
In view of the patent transfer among layers, three ob-
servations can be observed (Table 2). First, in sum, patent
within the core layer and between the core and semicore
layer occupies more than 87% of the total patent. Second, the
transfer of patents at the core layer is more concentrated. In
the two phases of 2000–2004 and 2005–2009, the proportion
of patent transfers at the core layer composed of the US, EU,
and Japan was 39.45% and 34.34%, respectively, which
decreased somewhat. From 2010 to 2019, due to China’s
entry into the core layer, the proportion has increased
sharply to 49.76% and 50.03%, respectively. ird, the
technology spillover effect from core layer to semicore is
obvious, manifesting in the increment in patent transfer
from 10.07% to 15%. e technology transfer from semicore
to core layer has dropped significantly, and the number of
patent transfers has increased from 39.56% to 22.4%.
4. Discussion
e 21
st
century has seen rapid development and wide
application of various emerging technologies such as the
Internet, big data, cloud computing, artificial intelligence,
and blockchain. e production, search, dissemination, and
application of knowledge have effectively broken through
the limitations of geographical distance. Innovation activi-
ties have expanded from within the organization to cross-
organizational and cross-regional networks, and gradually
evolved into a global network. As a result, the scale of the
GIPN expanded rapidly from a sparse network to a dense
and complex network. As the number of global patent
cooperation increase continually, the patent cooperation
network shows obvious small-world characteristic, and the
integration of countries is high, which helps to obtain new
information and new resources, and strengthen patent co-
operation and innovation among countries.
Our results also suggest some important implications.
First, the patents dataset is useful for exploring the global
trends of technological diffusion. e strength of network
analysis is that it describes the relationships among coun-
tries. In this study, network analysis presented not only
which countries have higher technological capabilities but
also how countries are mutually connected for technological
collaboration or transfer. Since 2000, the GIPN has gradually
lost its scale-free feature, and the small-world feature has
been continuously strengthened [24].
Second, intellectual property is an important indicator to
forecast global investment flow and entrepreneurial envi-
ronment. As innovation shows the property of clustering,
which provides an important driving force and encour-
agement for entrepreneurship [25]. In a long term, the
pattern of the GIPN has presented a triangle structure with
the three core regions of “US-EU-Japan.” e triangle
structure accumulated a large number of patents. Since 2005,
the pattern of the GIPN has presented a diamond structure
with the four core regions of the US, EU, Japan, and China,
as the apex. Although the US, EU, and Japan have been the
core of the GIPN, Asian countries have gradually improved
their status in the cooperative innovation network and
gradually entered the core layer [26, 27]. Patent plays a key
role in transferring innovations and changing the social,
economic, and political system on a global level. rough
innovative production and technological cooperation,
countries gained chances to surpass their original innovative
production network system and achieve a certain degree of
leapfrog development. Existing studies have shown that
emerging marketing countries’ positions in the GIPN have
been raised gradually [27–29].
ird, the GIPN has been structured as a core-peripheral
structure. During the period of 2000–2009, the US has been
the absolute core in the GIPN. Our analysis also presents an
interesting observation that the countries’ positions in the
GIPN have been relatively stable. Specifically, the US, EU,
Japan, China, Australia, Brazil, Canada, China, India, Israel,
Mexico, Korea, Russia, Singapore, and Switzerland have
remained in the top positions in the GIPN. Enterprises,
especially multinational companies, are the mainstay of
innovation. Multinational companies transfer innovation to
various countries through R&D alliances, cooperation
agreements, subsidiaries, and affiliates. erefore, the GIPN
is an extension of the global production network [30]. In the
GIPN, information technology and industry are combined
to form a useful supplement to the internal innovation
activities of enterprises and their core competitiveness.
However, even so, in the industrial value chain, the inno-
vation function is the most difficult to transfer. In com-
parison, patent greatly influences global technology
collaboration among well-developed or major economically
powerful countries while less developed countries have less
potential to participate in the process of global technology
transfer. In other words, the division of labor in the global
8Complexity
Table 2: e percentage of patents among layers in GIPN.
Layer Percentage (%)
2000–2004 2005–2009 2010–2014 2015–2019
Core-core 39.45 34.34 49.76 50.03
Core-semicore 10.07 11.82 14.17 15.00
Core-semiperiphery 0.70 0.48 0.83 1.04
Core-periphery 0.07 0.26 0.26 0.22
Semicore-core 39.56 41.91 24.59 22.40
Semicore-semicore 4.40 5.94 2.87 2.64
Semicore-semiperiphery 0.56 0.47 0.42 0.40
Semicore-periphery 0.13 0.27 0.14 0.11
Semiperiphery-core 3.90 3.42 5.26 6.19
Semiperiphery-semicore 0.52 0.53 1.07 1.27
Semiperiphery-semiperiphery 0.08 0.04 0.12 0.15
Semiperiphery-periphery 0.03 0.03 0.05 0.05
Periphery-core 0.43 0.39 0.35 0.37
Periphery-semicore 0.07 0.08 0.09 0.10
Periphery-semiperiphery 0.02 0.01 0.01 0.02
Periphery-periphery 0.00 0.01 0.01 0.01
Sum 100 100 100 100
Figure 5: Core-periphery structure in the GIPN.
Complexity 9
industrial value chain is still difficult to change. erefore,
similar to the global production network, only several
countries determine the global innovation division of labor
and the direction of technological development [31]. In
addition, another point of view explaining this phenomenon
is that the multilateral intellectual property system under the
WTO is rooted in neoliberalism, which is the main eco-
nomic ideology of western industrial capitalism. Although
neoliberalism supports the use of multilateral rules and
policies (such as proprietary intellectual property) as tools to
promote technology transfer and commodities, empirical
results show that the impact of such support is very limited
[32].
5. Conclusion
is analysis explored the structure and evolution charac-
teristics from both static and dynamic aspects. Static analysis
reveals the overall distribution characteristics of the GIPN,
while dynamic evolution analysis effectively identifies the
evolution characteristics and development trends of the
GIPN.
is analysis has aimed to explore the structural char-
acteristics and evolution of the GIPN with the application of
the patent dataset. Results gained from our analysis show
that (1) in the period 2000–2019, the scale of the GIPN is
expanding rapidly, gradually growing from a sparse network
to a dense and complex network. (2) Patent application is
unevenly distributed in the globe. e US, China, and
Canada have been the top countries flowing in while Japan,
Korea, EU, and Switzerland has been the main countries
flowing out. (3) Some Asian countries have raised their
position largely in the GIPN. ese Asian countries include
Viet Nam, ailand, Malaysia, Singapore, and Indonesia. (4)
e GIPN shows an obvious “core-periphery” pattern. e
distribution pattern is unevenly distributed in space and
presents a quadrilateral structure with the four core regions
of “US, Japan, EU, and China” as the apex.
is analysis contributed to providing new insights both
methodologically and theoretically. From the methodolog-
ical view, based on the dynamic and static analysis of the
GIPN, this analysis enriches network characteristics at the
global scale and deepens the understanding of the global
intellectual property transfer mechanism. From a theoretical
view, given that the core of the GIPN is still distributed in a
small number of core countries, countries are maintaining
cooperation with core countries. e country at the key
connection point can control and promote the technical
exchange of nodes in the network. By linking different
technologies together, technical barriers can be overcome,
and the integration and innovation of different technologies
can be accelerated. In addition, a deepening cooperation
with countries in the core layer is also proposed in the future.
6. Limitations and Future Research
e analysis suffers from limitations in both methodological
and dataset perspectives. First, the analysis suffers from
limitations in a dataset perspective since we fail to
distinguish the types of innovative areas, which may loss the
shifting knowledge of intellectual property among the
various fields. Second, we use the data of application rather
than completed and approved. As in recent years, countries
have become more and more strict about the protection of
high-tech products and the term from application to ap-
proval is almost two years or more. e long application
cycle may cause delays in changes in the GIPN. ird, this
analysis did not focus on countries which have low rankings
in the GIPN, paying little attention to the development of
marginal countries in the GIPN. e overall goal of this
analysis was to estimate the evolution structure of the GIPN.
From the information collected from WIPO, we found that
all the main country which occupy the main positions in
global economy have been included. us, we believe the
above effect to be of minor relevance. Future studies may
consider the GIPN’s changes in the turbulent year of 2020.
At the same time, the GIPN will focus on various industries
[33], consider the deep reasons for the evolution of inno-
vation networks, and explore the deep relationship between
the world innovation pattern, transnational knowledge
capital flow and international economic background.
Data Availability
e data collected during the study are freely available from
the World Intellectual Property Organization (https://www.
wipo.int/portal/en/index.html).
Conflicts of Interest
e authors declare no conflict of interest.
Acknowledgments
is research was supported by the Beijing Philosophy and
Social Science Foundation (No. 21JJC023), Key Project
supported by Beijing Municipal Education Commission
(No. SZ202110011006), and National Natural Science
Foundation of China (No. 42101210).
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