Online social networking services involve communication activities between large number of individuals over the public Internet and their crawled records are often regarded as proxies of real (i.e., offline) interaction structure. However, structure observed in these records might differ from real counterparts because individuals may behave differently online and non-human accounts may even participate. To understand the difference between online and real social networks, we investigate an empirical communication network between users on Twitter, which is perhaps one of the largest social networking services. We define a network of user pairs that send reciprocal messages. Based on the correlation between degree of adjacent nodes observed in this network, we hypothesize that this network differs from conventional understandings in the sense that there is a small number of distinctive users that we call outsiders. Outsiders do not belong to any user groups but they are connected with different groups, while not being well connected with each other. We identify outsiders by maximizing the degree assortativity coefficient of the network via node removal, thereby confirming that local structural properties of outsiders identified are consistent with our hypothesis. Our findings suggest that the existence of outsiders should be considered when using Twitter communication networks for social network analysis.

Using the direct current power flow model, we study cascading failures and their spatial and temporal properties in the U.S. Western Interconnection (USWI) power grid. We show that yield (the fraction of demand satisfied after the cascade) has a bimodal distribution typical of a first-order transition. The single line failure leads either to an insignificant power loss or to a cascade which causes a major blackout with yield less than 0.8. The former occurs with high probability if line tolerance α (the ratio of the maximal load a line can carry to its initial load) is greater than 2, while a major blackout occurs with high probability in a broad range of 1 < α < 2. We also show that major blackouts begin with a latent period (with duration proportional to α) during which few lines overload and yield remains high. The existence of the latent period suggests that intervention during early stages of a cascade can significantly reduce the risk of a major blackout. Finally, we introduce the preferential Degree And Distance Attachment model to generate random networks with similar degree, resistance, and flow distributions to the USWI. Moreover, we show that the Degree And Distance Attachment model behaves similarly to the USWI against failures.

Latent stochastic blockmodels are flexible statistical models that are widely used in social network analysis. In recent years, efforts have been made to extend these models to temporal dynamic networks, whereby the connections between nodes are observed at a number of different times. In this paper, we propose a new Bayesian framework to characterize the construction of connections. We rely on a Markovian property to describe the evolution of nodes' cluster memberships over time. We recast the problem of clustering the nodes of the network into a model-based context, showing that the integrated completed likelihood can be evaluated analytically for a number of likelihood models. Then, we propose a scalable greedy algorithm to maximize this quantity, thereby estimating both the optimal partition and the ideal number of groups in a single inferential framework. Finally, we propose applications of our methodology to both real and artificial datasets.

In this introduction to the special section on globalization, regionalization, and multi-polarity, we review network analysis applications to the study of globalization as a complex and multi-dimensional phenomenon and we explore the frontiers of our knowledge about the network properties of global systems. We focus on the global economic (trade and investment), political, and migration systems.

In this paper, we study how the topology of the International Trade Network (ITN) changes in geographical space, and along time. We employ geographical distance between countries in the world to filter the links in the ITN, building a sequence of subnetworks, each one featuring trade links occurring at similar distance. We then test if the assortativity and clustering of ITN subnetworks changes as distance increases, and we find that this is indeed the case: distance strongly impacts, in a non-linear way, the topology of the ITN. We show that the ITN is disassortative at long distances, while it is assortative at short ones. Similarly, the main determinant of the overall high-ITN clustering level are triangular trade triples between geographically close countries. This means that trade partnership choices and trade patterns are highly differentiated over different distance ranges, even after controlling for the economic size and income per capita of trading partners, and it is persistent over time. This evidence has relevant implications for the non-linear evolution of globalization.

Trade in oil has undergone significant changes in the last 20 years: technical progress has allowed the exploitation of new and previously untapped fields; the emergence of new large oil importers, such as China, has shifted the traditional patterns of demand and supply, while the desire to diversify energy sources has favored the emergence of new suppliers. This paper compares the topological properties of the network of crude oil trade over the period 1995–2014. The analysis covers both aggregate measures (such as network density and centralization) and node-specific indicators (e.g. centrality) that allow to uncover the rise (demise) of new (old) important players. Accounting for the position of each country within the network provides valuable information above and beyond traditional measures such as market shares. To investigate whether oil trade has experienced a process of globalization or, rather, regionalization, we look at the community structure of the network: the number of communities increases in the aftermath of the global financial crisis, but then goes back to its historical values. Something similar happens to the average geographic distance within each community, showing that along a regional component there are also strategic/political considerations at play. Econometric analysis suggests that high oil prices increase the likelihood that high-production-cost exporters play a more central role in the network, thus reducing the power of traditional suppliers.

This paper explores the structural changes in the international organization of automotive production since the early 1990s. We apply descriptive network measures to international trade data for the period 1993–2013 for three automotive component groups with different technological intensity, with the aim of understanding (1) how the automotive international trade network has changed since the beginning of the 1990s; (2) whether regionalization has increased over time; (3) how the role that rising powers play within and between regions interacts with regionalization patterns. Our findings suggest that the structure of trade has changed significantly over the last two decades in all components. The network for electric and electrical parts and engines has evolved to become more hierarchical, with a cohesive core tied to hangers-on in the periphery, while the opposite has happened for rubber and metal. Regionalization patterns also show important differences across components. Finally, we have found an apparent association between the strengthening of regionalization patterns and the role played by traditional players and rising powers.

International trade has been increasingly organized in the form of global value chains (GVCs). In this paper, we provide a new method for comparing GVCs across countries and over time. First, we use the World Input–Output Database (WIOD) to construct both the upstream and the downstream global value networks. Second, we introduce a network-based measure of node similarity to compare the GVCs between any pair of countries for each sector and each year available in the WIOD. Our network-based similarity is a better measure for node comparison than the existing ones because it takes into account all the direct and indirect relationships between the country–sector pairs, is applicable to both directed and weighted networks with self-loops, and takes into account externally defined node attributes. As a result, our measure of similarity reveals the most intensive interactions among the GVCs across countries and over time. From 1995 to 2011, the average similarity between sectors and countries have clear increasing trends, which are temporarily interrupted by the recent economic crisis. This measure of the similarity of GVCs provides quantitative answers to important questions about dependency, sustainability, risk, and competition in the global production system.

In the original publication of this article (Abbate et al. 2018), a disclaimer was omitted. The authors wish to state the following:

The views expressed in this study are the authors' and do not necessarily reflect those of the Swiss National Bank.

The authors regret the omission.