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.

Deferred prosecution agreements (DPAs) are a legal tool for the nontrial resolution of cases of corruption. Each DPA is accompanied by a Statement of Facts that provides detailed and publicly available textual records of the given cases, including summarized evidence of who was involved, what they committed, and with whom. These statements can be translated into networks amenable to social network analysis allowing an analysis of the structure and dynamics of each case. In this study, we show how to extract information about which actors were involved in a given case, the relations and interactions among these actors (e.g., communication or payments), and their relevant individual attributes (gender, affiliation, and sector) from five Statements of Fact. We code the extracted information manually with two independent coders and subsequently, we assess the inter-coder reliability. For assessing the coding reliability of nodes and attributes, we use a matching coefficient, whereas for assessing the coding reliability of ties, we construct a network from the coding of each coder and subsequently calculate the graph correlations of the two resulting networks. The coding of nodes and ties in the five extracted networks turns out to be highly reliable with only slightly lower coding reliability in the case of the largest network. The coding of attributes is highly reliable as well, although it is prone to missing data on actors’ gender. We conclude by discussing the flexibility of our data collection framework and its extension by including network dynamics and nonhuman actors (such as companies) in the network representation.

Social Network Analysis (SNA) enables the fine scale of animal sociality and population structure to be quantified. SNA is widely applied to questions relating to behavioural ecology but has seen little use in the application to zoo animal management, despite its clear potential. Investment in social bonds between individuals positively affects health status, welfare state, long-term fitness and lifetime reproductive output. Such positive affective states can be maintained consistently within captive situations if more information is known about the social preferences of the individuals that are kept. Disruption to social bonds may lead to impoverished welfare and stress to individuals which have seen their social support compromised. The patterning of social relationships between individuals also influences how space is utilised and how animals interact with resources provided for them. With more detailed knowledge of the social structure of a group or population, social groupings (for example, for captive breeding) can be specifically designed to minimise social stress. Likewise, enhancing the chances of successful reproduction can be achieved if we understand the role that each individual within a network plays and how these roles may impact on the behaviour of others. This paper discusses key aspects of SNA applicable to zoo-based researchers wishing to investigate the social lives of zoo animals. We present a review of how SNA can be used to assess social behaviour and highlight directions for future research. Our aim is to stimulate new research to ultimately improve our understanding of reproductive success, decision-making, group leadership, animal health and enclosure use. We conclude that what can be learned about the dynamics of social zoo-housed species using SNA can directly impact on husbandry decisions and help underpin excellent standards of animal welfare.

In our Information Technology (IT) based societies, social media plays an important role in communications and social networks for COVID-19. This study explores social responses for COVID-19 in North America, which is the most severe continent affected by the COVID-19 pandemic. This study employs social network analysis for Twitter among the US, Canada, and Mexico. This study finds that the 3 countries show different characteristics of social networks for COVID-19. For example, the Prime Minister plays the second most important role in the Canadian networks, whereas the Presidents play the most significant role in them, in the US, and Mexico. WHO shows a pivotal effect on social networks of COVID-19 in Canada and the US, whereas it does not affect them in Mexico. Canadians are interested in COVID-19 apps, the American people criticize the president and administration as incompetent in terms of COVID-19, and the Mexican people search for COVID-19 cases and the pandemic in Mexico. This study shows that governments and disease experts should understand social networks and communications of social network services, to develop effective COVID-19 policies according to the characteristics of their country.