Insights from Social Network Analysis reveal that the structure of the social network surrounding international courts is important for these courts’ ability to secure compliance with their judgments and by this to initiate social change. International courts like the European Court of Human Rights (ECtHR) invest growing resources in shaping their networks, recognising that these networks are necessary tools that can help them to influence society. This paper will focus on the ways social network analysis can facilitate a better understanding of the ECtHR. The paper explains how certain characteristics of the network surrounding the ECtHR determine the ultimate social impact of the court.

Increasing transboundary crises necessitate the development of crisis management capabilities that transcend boundaries. In such situations, inter-governmental and cross-functional collaboration has become a common practice to address the complexities of governance challenges. This study employs Social Network Analysis to examine the structure, function, and evolution of policy collaboration networks in China in response to COVID-19 and SARS. Since the SARS outbreak, China has embraced a collaborative governance approach, considering the transboundary nature of COVID-19. This approach has led to the involvement of numerous specialized organizations engaged in economic and social development, contributing to the establishment of a larger and more loosely connected collaboration network. While the health department bears the primary responsibility for coordinating public health emergency management, diverse organizations with social governance and economic management functions have also emerged as key actors, providing crucial anti-epidemic information, knowledge, and resources to address this significant cross-border crisis.

While prior studies have barely explored social interaction for COVID-19 across Asia, this study highlights how people interact with each other for the COVID-19 pandemic among India, Japan, and South Korea based on social network analysis by employing NodeXL for Twitter between July 27 and July 28, 2020. This study finds that the Ministry of Health and Prime Minister of India, news media of Japan, and the president of South Korea play the most essential role in social networks in their country, respectively. Second, governmental key players play the most crucial role in South Korea, whereas they play the least role in India. Third, the Indian are interested in COVID-19 deaths, the Japanese care about the information of COVID-19 patients, and the South Korean focus on COVID-19 vaccines. Therefore, governments and disease experts should explore their social interaction based on the characteristics of social networks to release important news and information in a timely manner.

This study highlights key players for COVID-19 in Brazil, Peru, Colombia, Chile, Argentina, and Ecuador by employing social network analysis for Twitter. This study finds that key players in Latin America play various roles in COVID-19 social networks, differing from country to country. For example, Brazil has no Latin key players, whereas Colombia and Ecuador have 8 Latin key players in the top 10 key players. Secondly, the role of governmental key players also varies across different countries. For instance, Peru, Chile, Argentina, and Ecuador have the governmental key player as the top key player, whereas Brazil and Colombia have the news media key player as the first. Thirdly, each country shows different social networks according to groups. For instance, Colombia exhibits the most open social networks among groups, whereas Brazil shows the most closed social networks among the 6 Latin countries. Fourthly, several top tweeters are common across the 6 Latin American countries. For example, Peru and Colombia have caraotadigital (Venezuelan news media), and Chile and Argentina have extravzla (Venezuelan news media) as the top tweeter.

In the Salish Sea region, labret adornment with lip plugs signify particular identities, and they are interpreted as emblematic of both membership in horizontal relationships and achieved status for traditional cultures associated with labret wearing on the Northwest Coast (NWC) of North America. Labrets are part of a shared symbolic language in the region, one that we argue facilitated access to beneficial horizontal relationships (e.g., Angelbeck and Grier 2012; Rorabaugh and Shantry 2017). We employ social network analysis (SNA) to examine labrets from 31 dated site components in the Salish Sea region spanning between 3500 and 1500 cal BP. Following this period, the more widely distributed practice of cranial modification as a social marker of status developed in the region. The SNA of labret data shows an elaboration and expansion of antecedent social networks prior to the practice of cranial modification. Understandings of status on the NWC work backward from direct contact with Indigenous societies. Labret wearing begins at the Middle-Late Holocene transition, setting an earlier stage for the horizontal social relationships seen in the ethnohistoric period. These findings are consistent with the practice as signifying restricted group membership based on affinal ties and achieved social status.

Thriving at work is closely related to the way employees are embedded in their social contexts, such as the structure of their communication relations with coworkers. In previous research, communication relations have been found to negatively relate to thriving at work. However, social network theory suggests that communication relations are beneficial in obtaining resources in the workplace, which might increase thriving at work. To reconcile the seemingly conflicting mechanisms, we draw on social network theory to unpack the mechanisms underlying communication relations by considering the instrumental and expressive roles. Using a structural equation model, we investigate the indirect effects of communication networks on thriving at work via advice-seeking networks (instrumental) and friendship networks (expressive). Our findings indicate communication relations are negatively related to thriving at work via advice-seeking relations, but are positively related to thriving at work via friendship relations.

Under what conditions do countries lose their status as the leading global financial center? Some scholars argue that such shifts follow shortly after transitions in the distribution of other key capabilities (e.g. GDP), while others argue that path dependence or other more bespoke capabilities might be able to sustain financial leadership long after decline in other capabilities. This paper aims to understand the causes of the Anglo-American financial transition. I argue that the ability to manage political risk for investors is critical to the position of countries as financial entrepôts. In the case of British financial leadership, I argue that Britain’s position as an entrepôt hinged on its power projection capability, which enabled Britain to limit political risk for investors in ways that other states could not replicate. The gradual loss of those capabilities, in turn, saw Britain eventually become overshadowed by the United States. I support my claims with a TERGM analysis of the interwar sovereign debt network.

Social network analysis is known to provide a wealth of insights relevant to many aspects of policymaking. Yet, the social data needed to construct social networks are not always available. Furthermore, even when they are, interpreting such networks often relies on extraneous knowledge. Here, we propose an approach to infer social networks directly from the texts produced by actors and the terminological similarities that these texts exhibit. This approach relies on fitting a topic model to the texts produced by these actors and measuring topic profile correlations between actors. This reveals what can be called “hidden communities of interest,” that is, groups of actors sharing similar semantic contents but whose social relationships with one another may be unknown or underlying. Network interpretation follows from the topic model. Diachronic perspectives can also be built by modeling the networks over different time periods and mapping genealogical relationships between communities. As a case study, the approach is deployed over a working corpus of academic articles (domain of philosophy of science; N=16,917).

Despite being protected under the law, illegal trade in tortoises and freshwater turtles is common in India, with different species being trafficked for different markets. Indian species of tortoises and hard-shell turtles are predominantly trafficked for the pet trade and soft-shell turtles for the meat trade. Given their distinct markets, the operation of trade may vary between these different groups of tortoises and freshwater turtles, thereby necessitating different types of interventions. However, a systematic examination of illegal trade in tortoises and freshwater turtles that takes into account the differences between these markets is currently lacking. Here we compare the supply networks of tortoises/hard-shell turtles (in demand for pet trade) vs soft-shell turtles (meat trade), using information from 78 and 64 seizures, respectively, that were reported in the media during 2013–2019. We used social network analysis to compare the two networks and the role of individual nodes (defined as locations at the district or city scale) within these networks. We found that the tortoise/hard-shell turtle network had a larger geographical scale, with more international trafficking links, than the soft-shell turtle network. We recorded convoluted smuggling routes in tortoise/hard-shell turtle trafficking, whereas soft-shell turtle trafficking was uni-directional from source to destination. Within both networks, we found that a few nodes played disproportionately important roles as key exporting, importing or transit nodes. Our study provides insights into the similarities and differences in the illegal supply networks of different groups of tortoises and freshwater turtles, in demand for different markets. We highlight the need for intervention strategies tailored to address the illegal trade in each of these groups.

Palaeoenvironmental data indicate that the climate of south-western Madagascar has changed repeatedly over the past millennium. Combined with socio-political challenges such as warfare and slave raiding, communities continually had to mitigate against risk. Here, the authors apply social network analysis to pottery assemblages from sites on the Velondriake coast to identify intercommunity connectivity and changes over time. The results indicate both continuity of densely connected networks and change in their spatial extent and structure. These network shifts coincided with periods of socio-political and environmental perturbation attested in palaeoclimate data and oral histories. Communities responded to socio-political and environmental risk by reconfiguring social connections and migrating to areas of greater resource availability or political security.

In relational event networks, endogenous statistics are used to summarize the past activity between actors. Typically, it is assumed that past events have equal weight on the social interaction rate in the (near) future regardless of the time that has transpired since observing them. Generally, it is unrealistic to assume that recently past events affect the current event rate to an equal degree as long-past events. Alternatively one may consider using a prespecified decay function with a prespecified rate of decay. A problem then is that the chosen decay function could be misspecified yielding biased results and incorrect conclusions. In this paper, we introduce three parametric weight decay functions (exponential, linear, and one-step) that can be embedded in a relational event model. A statistical method is presented to decide which memory decay function and memory parameter best fit the observed sequence of events. We present simulation studies that show the presence of bias in the estimates of effects of the statistics whenever the decay, as well as the memory parameter, are not properly estimated, and the ability to test different memory models against each other using the Bayes factor. Finally, we apply the methodology to two empirical case studies.