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.

Symptom and Age Homophilies in SARS-CoV-2 Transmission Networks during the Early Phase of the Pandemic in Japan

Kanagawa and Hokkaido were affected by COVID-19 in the early stage of the pandemic. Japan’s initial response included contact tracing and PCR analysis on anyone who was suspected of having been exposed to SARS-CoV-2. In this retrospective study, we analyzed publicly available COVID-19 registry data from Kanagawa and Hokkaido (n = 4392). Exponential random graph model (ERGM) network analysis was performed to examine demographic and symptomological homophilies. Age, symptomatic, and asymptomatic status homophilies were seen in both prefectures. Symptom homophilies suggest that nuanced genetic differences in the virus may affect its epithelial cell type range and can result in the diversity of symptoms seen in individuals infected by SARS-CoV-2. Environmental variables such as temperature and humidity may also play a role in the overall pathogenesis of the virus. A higher level of asymptomatic transmission was observed in Kanagawa. Moreover, patients who contracted the virus through secondary or tertiary contacts were shown to be asymptomatic more frequently than those who contracted it from primary cases. Additionally, most of the transmissions stopped at the primary and secondary levels. As expected, significant viral transmission was seen in healthcare settings.

Japanese Network Economy-ERGM analysis of the “Big Six”

The paper analyses the network of relationships between large companies that defines the Japaneseeconomy using ERGM (Exponential Random Graph Model) method. The Japanese economy, becauseof the presence of keiretsu groups and other corporate groups, is an excellent example of what canhappen to a country’s economy if large corporations and financial institutions in the market operate ina closely interconnected, highly centralized and dependent network. This is an important issue, becausethis economic practice is happening globally right now. Therefore, the interpretation of the Japanesenetwork and the analysis of its economic performance also reflect the long-term negative and positiveeffects of this trend.

Does Whom Patients Sit Next to During Hemodialysis Affect Whether They Request a Living Donation?

Background: The seating arrangement of in-center hemodialysis is conducive to patients forming a relationship and a social network. We examined how in-center hemodialysis clinic seating affected patients forming relationships, whether patients formed relationships with others who have similar transplant behaviors (homophily), and whether these relationships influenced patients (social contagion) to request a living donation from family and friends outside of the clinic. Methods: In this 30-month prospective cohort study, we observed the relationships of 46 hemodialysis patients in a hemodialysis clinic. Repeated participant surveys assessed in-center transplant discussions and living donor requests. A separable temporal exponential random graph model estimated how seating, demographics, in-center transplant discussions, and living donor requests affected relationship formation via sociality and homophily. We examined whether donation requests spread via social contagion using a susceptibility-infected model. Results: For every seat apart, the odds of participants forming a relationship decreased (OR 0.74, 95% confidence interval CI [0.61, 0.90], p = 0.002). Those who requested a living donation tended to form relationships more than those who did not (sociality, OR 1.6, CI 95% [1.02, 2.6]; p = 0.04). Participants who discussed transplantation in-center were more likely to form a relationship with another participant who discussed transplantation than with someone who did not discuss transplantation (homophily, OR 1.9, CI 95% [1.03, 3.5]; p = 0.04). Five of the 36 susceptible participants made a request after forming a relationship with another patient. Conclusions: Participants formed relationships with those whom they sat next to and had similar transplant behaviors. The observed increase in in-center transplant discussions and living donation requests by the hemodialysis clinic social network members was not because of social contagion. Instead, participants who requested a living donation were more social, formed more relationships within the clinic, and discussed transplantation with each other as a function of health-behavior homophily.

Statistical Inference for Multilayer Networks in Political Science

Interactions between units in political systems often occur across multiple relational contexts. These relational systems feature interdependencies that result in inferential shortcomings and poorly-fitting models when ignored. General advancements in inferential network analysis have improved our ability to understand relational systems featuring interdependence, but developments specific to working with interdependence that cross relational contexts remain sparse. In this paper, I introduce a multilayer network approach to modeling systems comprising multiple relations using the exponential random graph model. In two substantive applications, the first a policy communication network and the second a global conflict network, I demonstrate that the multilayer approach affords inferential leverage and produces models that better fit observed data.