41 research outputs found
Network communities within and across borders
We investigate the impact of borders on the topology of spatially embedded
networks. Indeed territorial subdivisions and geographical borders
significantly hamper the geographical span of networks thus playing a key role
in the formation of network communities. This is especially important in
scientific and technological policy-making, highlighting the interplay between
pressure for the internationalization to lead towards a global innovation
system and the administrative borders imposed by the national and regional
institutions. In this study we introduce an outreach index to quantify the
impact of borders on the community structure and apply it to the case of the
European and US patent co-inventors networks. We find that (a) the US
connectivity decays as a power of distance, whereas we observe a faster
exponential decay for Europe; (b) European network communities essentially
correspond to nations and contiguous regions while US communities span multiple
states across the whole country without any characteristic geographic scale. We
confirm our findings by means of a set of simulations aimed at exploring the
relationship between different patterns of cross-border community structures
and the outreach index.Comment: Scientific Reports 4, 201
Statistical physics of network communities in economic systems
In the last decade, the study of big networked systems has received a great deal of attention
thanks to the increased availability of large datasets and the technology to analyze them. To unravel
regularities and behaviours from his enormous quantity of data and supply suitable models, we need
appropriate tools, one of them being community detection. Finding meaningful communities in a
networks is still a diffcult task but essential to unveil functional relations between the parts.
The research presented here has been carried out focusing on community detection; in particular
were considered cases where the spatial component was relevant or intrinsic. It is indeed true that,
nowadays, many systems, represented as complex networks, are affected, more or less naturally,
by the geographical distance, location and organization. This holds true even for economic events:
it has been proved that trade and exchanges between countries are necessarily suffocated by the
geographical proximity or impeded by natural obstacles.
Still, community detection alone is not sufficient to describe the whole picture, since it gives
no information about the internal structure of a community. Therefore we developed the novel
core detection method, natural counterpart of the community detection algorithm and meant to be
performed alongside it, which is, at the same time, simple and powerful.
We aim to apply community detection and core detection methodologies to the analysis of the
global market and its functioning, in order to understand the origin of economic turmoils and critical
events.
In this work we analyze different economic systems from a complex network perspective and
find some interesting results: we study patent data in order to measure internationalization of
European countries and assess the effectiveness of EU policies; we examine the dynamics of network
effects on the performances of individual countries and trade relationships in the International Trade
Network; we represent World Input-Output data as an interdependent complex network and study
its properties, showing evidence of the crisis .
Thanks to both community and core detection, we are able to have a deeper insight on the
inner workings of community formation, we can identify the leading members in a group and reveal
in
uence basins, unknown otherwise
Community core detection in transportation networks
This work analyses methods for the identification and the stability under
perturbation of a territorial community structure with specific reference to
transportation networks. We considered networks of commuters for a city and an
insular region. In both cases, we have studied the distribution of commuters'
trips (i.e., home-to-work trips and viceversa). The identification and
stability of the communities' cores are linked to the land-use distribution
within the zone system, and therefore their proper definition may be useful to
transport planners.Comment: 8 pages, 13 figure
Examining the impact of destructive acts in marketing channel relationships
<p>The economies are arranged by rows and the industries are arranged by columns. Each color represents a community detected, except that the black color indicates the isolated nodes with only self-loop. Within each community, the top 3 core economy-industry pairs are identified. The first place is with thick and solid border. The second place is with thick and dashed border. The third place is with border and texture.</p
Spatial correlations in attribute communities
Community detection is an important tool for exploring and classifying the
properties of large complex networks and should be of great help for spatial
networks. Indeed, in addition to their location, nodes in spatial networks can
have attributes such as the language for individuals, or any other
socio-economical feature that we would like to identify in communities. We
discuss in this paper a crucial aspect which was not considered in previous
studies which is the possible existence of correlations between space and
attributes. Introducing a simple toy model in which both space and node
attributes are considered, we discuss the effect of space-attribute
correlations on the results of various community detection methods proposed for
spatial networks in this paper and in previous studies. When space is
irrelevant, our model is equivalent to the stochastic block model which has
been shown to display a detectability-non detectability transition. In the
regime where space dominates the link formation process, most methods can fail
to recover the communities, an effect which is particularly marked when
space-attributes correlations are strong. In this latter case, community
detection methods which remove the spatial component of the network can miss a
large part of the community structure and can lead to incorrect results.Comment: 10 pages and 7 figure
Global value trees
The fragmentation of production across countries has become an important feature of the globalization in recent decades and is often conceptualized by the term “global value chains” (GVCs). When empirically investigating the GVCs, previous studies are mainly interested in knowing how global the GVCs are rather than how the GVCs look like. From a complex networks perspective, we use the World Input-Output Database (WIOD) to study the evolution of the global production system. We find that the industry-level GVCs are indeed not chain-like but are better characterized by the tree topology. Hence, we compute the global value trees (GVTs) for all the industries available in the WIOD. Moreover, we compute an industry importance measure based on the GVTs and compare it with other network centrality measures. Finally, we discuss some future applications of the GVTs
The rise of China in the international trade network: a community core detection approach
Theory of complex networks proved successful in the description of a variety of static networks ranging from biology to computer and social sciences and to economics and
finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995-2011. We find rich dynamics over time both inter- and intra-communities. Most importantly, we have a multilevel description of the
evolution where the global dynamics (i.e., communities disappear or reemerge) tend to be correlated with the regional dynamics (i.e., community core changes between
community members). In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. Moreover,
simulation results show that the global dynamics can be generated by a preferential attachment mechanism both inter- and intra- communities
World input-output network
Production systems, traditionally analyzed as almost independent national systems, are increasingly connected on a global scale. Only recently becoming available, the World Input-Output Database (WIOD) is one of the first efforts to construct the global multi-regional input-output (GMRIO) tables. By viewing the world input-output system as an interdependent network where the nodes are the individual industries in different economies and the edges are the monetary goods flows between industries, we analyze respectively the global, regional, and local network properties of the so-called world input-output network (WION) and document its evolution over time. At global level, we find that the industries are highly but asymmetrically connected, which implies that micro shocks can lead to macro fluctuations. At regional level, we find that the world production is still operated nationally or at most regionally as the communities detected are either individual economies or geographically well defined regions. Finally, at local level, for each industry we compare the network-based measures with the traditional methods of backward linkages. We find that the network-based measures such as PageRank centrality and community coreness measure can give valuable insights into identifying the key industries
Top 20 industries identified by the four methods for selected years.
<p>The first is the Laumas method of backward linkages, <b>w</b>. The second is the eigenvector method of backward linkages, <b>e</b>. The third is PageRank centrality, <i>PR</i>. The fourth is community coreness measure ∣d<i>Q</i>∣.</p