Socioeconomic Networks and Network Science Workshop 2022

Recently, global supply chains are often disrupted because of trade policies and natural disasters. This study simulates the effect of disruption of imports from and exports to various regions on the total production of Japanese firms, incorporating propagation of the economic effect through domestic supply chains at the firm level. We find that the negative effect of disruption of intermediate imports grows exponentially as its duration and level increases because of downstream propagation. In particular, disruption of imports of electrical parts and components from Asia including China largely affects the manufacturing production of Japanese firms. In addition, the negative effect of disruption of imports from a particular region is more closely related to how importers are linked with domestic firms than the import value from the region. Furthermore, the negative effect of import disruption can be largely mitigated by reorganization of domestic supply chains. Our findings suggest that when trade restrictions are to be imposed, the economic loss can vary substantially depending on their target industries, duration, and level, and the available substitutions.

Production networks are integral to economic dynamics, yet dis-aggregated network data on inter-firm trade is rarely collected and often proprietary. Here we situate company-level production networks within a wider space of networks that are different in nature, but similar in local connectivity structure. Through this lens, we study a regional and a national network of inferred trade relationships reconstructed from Dutch national economic statistics and re-interpret prior empirical findings. We find that company-level production networks have so-called functional structure, as previously identified in protein-protein interaction (PPI) networks. Functional networks are distinctive in their over-representation of closed squares, which we quantify using an existing measure called spectral bipartivity. Shared local connectivity structure lets us ferry insights between domains. PPI networks are shaped by complementarity, rather than homophily, and we use multi-layer directed configuration models to show that this principle explains the emergence of functional structure in production networks. Companies are especially similar to their close competitors, not to their trading partners. Our findings have practical implications for the analysis of production networks and give us precise terms for the local structural features that may be key to understanding their routine function, failure, and growth.

The World Trade Web (WTW) is the network of international trade relationships among world countries. Characterizing both the local link weights (observed trade volumes) and the global network structure (large-scale topology) of the WTW via a single model is still an open issue. While the traditional Gravity Model (GM) successfully replicates the observed trade volumes by employing macroeconomic properties such as GDPs and geographic distances, it unfortunately predicts a fully connected network, returning a completely unrealistic topology of the WTW. To overcome this problem, two different classes of models have been introduced in econometrics and statistical physics. Econometric approaches interpret the traditional GM as the expected value of a probability distribution that can be chosen arbitrarily and tested against alternative distributions. Statistical physics approaches construct maximum-entropy probability distributions of (weighted) graphs from a chosen set of measurable, structural constraints and test distributions resulting from different constraints. Here we compare and integrate the two approaches by considering a class of maximum-entropy models that can incorporate macroeconomic properties used in standard econometric models. We find that the integrated approach achieves an overall better performance than the purely econometric one - a result suggesting that the maximum-entropy construction can indeed serve as a viable econometric framework that allows topological constraints to be combined with dyadic, macroeconomic variables.

Human behavior is the potential and expressed capacity (mentally, physically, and socially) of human individuals or groups to respond to internal and external stimuli. We explore assortative matching as a typic human behavior in a virtual networked community. Inspired by Becker's marriage market model and Gale–Shapley algorithm, we present a NetLogo-based MAS(multi-agent system) to simulate human behaviors by various environmental parameter settings and gaming strategies. The experiment demonstrates the conditions of the internal and external factors which affect human behaviors in the network, e.g.: herd behavior, stochastic search equilibrium, etc. This work also shows the efficiency of the MAS simulation method associated with classic qualitative and quantitative social surveys for specific topics in socioeconomic research.

Modern scientific work, including writing papers and submitting research grant proposals, increasingly involves researchers from different institutions. In grant collaborations, it is known that institutions involved in many collaborations tend to densely collaborate with each other, forming rich clubs. Here we investigate higher-order rich-club phenomena in networks of collaborative research grants among institutions and their associations with research productivity. Using publicly available data from the National Science Foundation in the US, we construct a bipartite network of institutions and collaborative grants, which distinguishes among the collaboration with different numbers of institutions. By extending the concept and algorithms of the rich club for dyadic networks to the case of bipartite networks, we find rich clubs both in the entire bipartite network and the bipartite subnetwork induced by the collaborative grants involving a given number of institutions up to five. We also find that the collaborative grants within rich clubs tend to be more productive in a per-dollar sense than the control. Our results highlight advantages of collaborative grants among the institutions in the rich clubs.

Recent events such as the rejection of COVID-19 vaccination by a significant minority and the riot at the U.S. Capitol on January 6, 2021 have drawn increased attention to the impact which belief in conspiracy theories and 'fake news' can have on critical issues ranging from disease control to public safety and democratic stability. While these problems are far from new, the speed with which information can be disseminated across online social networks and the accompanying fragmentation of public information sources are often seen as factors which have increased the risks associated with misinformation and conspiracy belief in recent years. This study examines the creation and spread of 'fake news' about current events over social networks in Japan, and looks at whether specific online identities or network characteristics are associated with a higher receptiveness to conspiracy information.

The high development of emerging computing paradigms, such as Ubiquitous Computing, Mobile Computing, and Social Computing, has brought us a big change from all walks of our work, life, learning and entertainment. Especially, with the high accessibility of social networking services, more and more populations have been engaged into this kind of integration of real physical world and cyber digital space. In this talk, we concentrate on personalized big data analytics and social networking in cyber-social computing, specifically, discuss the research on scholarly big data, which is a large-scale collection of academic information, technical data, and collaboration relationships. Mechanisms and algorithms are introduced to facilitate the adoption of cyber-social computing paradigm that makes it easier for researchers to join collaborative research activities and share academic data across the highly interlaced cyber-social networks.