Understanding the Role of Humanistic Factors in Trade Network Evolution across the Belt and Road Initiative Countries Using the Exponential Random Graph Model

Humanistic factors have been playing increasingly significant roles in international trade. Recently, the Belt and Road Initiative (BRI) proposed by China has drawn worldwide attention. This paper examines the roles of humanistic factors in international trade networks across the BRI countries. Firstly, we analyzed the structural characteristics of the import trade network across the 61 BRI countries and subsequently adopted the cross-sectional exponential random graph model (ERGM) and temporal ERGM to analyze the role of different humanistic factors in the evolution of import trade network from the static and dynamic perspectives, respectively. The results show the following: (I) the network scale of the import trade across the BRI countries has been expanding, the network density of the trade has been increasing gradually, and the “small-world” characteristics of import network are gradually revealed; (II) all of the factors such as a common (official or spoken) language, a common legal origin, a common religious belief, and ever sibling relationship help the BRI countries establish closer import trade ties; and (III) the differences of trade liberalization and financial liberalization, gross domestic product (GDP), and population in different countries also contribute to the evolution of import trade network among the BRI countries, and the countries with relatively higher GDP and greater population are more active in the import trade network.

Evolving Crisis and Changing Networks: NGOs’ Stakeholder Engagement on the Global Refugee Crisis

Nongovernmental organizations (NGOs) increasingly utilize social media for strategic stakeholder engagement. This study proposes a network-oriented theoretical framework to understand how NGOs’ engagement with complex networks of stakeholders on the global refugee issue varies as the issue moves from low to high public attention stages. We draw from research on multistakeholder issue networks and issue niche theory and analyze a large-scale Twitter data set containing tweets from hundreds of organizations from more than 30 countries. This cross-national, longitudinal study tracks issue evolution and NGOs’ tie formation patterns among themselves and with complex stakeholders (i.e., government and media) as public attention to the refugee issue increases. The results of our exponential random graph models (ERGMs) show how cross-sector stakeholders interact dynamically and how different issue identities position NGOs uniquely in issue niches as the issue evolves. We also find that organizations’ country-level homophily influences tie formation. Theoretical and practical implications are discussed.

The Portuguese Hub of Companies in 1973 – Discussing the Corporate Concentration in the Portuguese Economy on the Eve of the April Revolution

The network of Portuguese companies in 1973 has been identified as a relevant element for understanding the economic structure of the country in the decade of 1970–1980. This network had been formed before 1974, during the dictatorship, but it remained after the Carnation Revolution. In spite of such research, this network has not yet been properly analysed, especially through adequate tools from network analysis. This work will detail this network, the different scores of centrality of each company, and their modular structures; it will also discuss estimates from exponential random graph models to identify significant attributes that explain the discovered flows of investment. This work will also detail the processes of vertical integration as well as the specificities of the identified oligopolies.

Women trafficking networks: Structure and stages of women trafficking in five Dutch small-scale networks

In this study, we investigated the relation between the different stages of women trafficking (i.e. recruitment, entrance, accommodation, labor, and finance) and the structure of five criminal networks involved in women trafficking in the Netherlands ( Ns ranging from 6 to 15). On the one hand, it could be argued that for efficiency and avoidance of being detected by law enforcement agencies, the network structure might align with the different stages, resulting in a cell-structured network with collaboration between actors within rather than across stages. On the other hand, criminal actors might prefer to collaborate and rely on a few others, whom they trust in order to circumvent the lack of formal opportunities to enforce collaboration and agreements, resulting in a core-periphery network with actors also collaborating across stages. Results indicate that three of the five networks were characterized by a core-periphery structure, whereas the two other networks exhibit a mixture of both a cell-structured and core-periphery network. Furthermore, using an Exponential Random Graph Model (ERGM), we found that actors were likely to form ties with each other in the stages of recruitment, accommodation, and exploitation, but not in the stages of transport and finance.

Networks of Global Social Policy Diffusion: The Effects of Culture, Economy, Colonial Legacies, and Geographic Proximity

The introductory chapter to the volume by Mossig, Windzio, Seitzer and Besche-Truthe defines the core concepts, such as diffusion and contagion, and gives an example of an application diffusion and contagion in epidemiology. The most important underlying functions, namely the logistic density and cumulative logistic density function, are explained, followed by a very brief introduction to the core concepts of event history analysis. In the network diffusion model, contagion, or, in other words, the adoption of information or innovation, is based on the concept of exposure which will be elaborated in this chapter. Finally, after describing and visualizing the four different networks and their correlations, exponential random graph models are used to analyze structural and substantive properties of these networks. The introduction concludes with a brief overview of the chapters.

Testing biological network motif significance with exponential random graph models

Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical significance of these motifs, and there are potential problems with standard methods for estimating this significance. Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs. ERGMs were first introduced into the bioinformatics literature over 10 years ago but have had limited application to biological networks, possibly due to the practical difficulty of estimating model parameters. Advances in estimation algorithms now afford analysis of much larger networks in practical time. We illustrate the application of ERGM to both an undirected protein–protein interaction (PPI) network and directed gene regulatory networks. ERGM models indicate over-representation of triangles in the PPI network, and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli and a yeast regulatory network. We also confirm, using ERGMs, previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features.