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.

Processual models of the early state envisioned hierarchical societies with stable social and political structures. More recent research, however, has questioned this vision. Here, the authors explore Middle Horizon (AD 700–1000) Wari state iconography to provide an example of early state social and political organisation from the Central Andes. Social network analysis (SNA) of human figures (‘agents’) depicted in Wari art identifies links between individual agents, as visualised on objects and between the objects’ findspots. The results suggest that the Wari state was more heterarchical than previously imagined. Similar applications of SNA could be used to explore the iconographic evidence of other early, pre-literate states around the world.

Participation in online courses has become essential for training language professionals in under-resourced contexts with skills in computer-assisted language learning (CALL) (Godwin-Jones, 2014). Most online CALL courses use asynchronous computer-mediated communication (ACMC) to facilitate meaningful learning for participants. Although participants’ sustained engagement with ACMC is the target, global realities of participants interfere with their participation levels. This article investigates participants’ engagement profiles in asynchronous online discussions in an 8-week CALL-based global online course developed and implemented by a team at Iowa State University. Using a case study approach, nine focal participants’ engagement profiles have been analyzed in terms of identifying patterns of engagement in the discussion posts and their relation to the types of discussion prompts. Then, social network analysis (SNA) and thematic analysis were employed to investigate patterns of interaction among the participants in the replies. The results indicated that engagement patterns observed in discussion posts overall aligned with the primary goals of prompt types. SNA further identified two participants as social mediators to connect participants with each other. These findings are significant in that they suggest the effectiveness of using ACMC to promote co-construction of knowledge for a global audience. This article also provides implications regarding the design of discussion prompts to help maximize participant engagement with course content.

Why do states participate in bilateral investment treaties (BITs)? In this article, I examine the role of indirect investment on BIT formation. Indirect investment flows are an important aspect of the global investment regime that are underexamined by research focused on direct flows only. Indirect flows play an important role in affecting incentives for BIT participation because firms channel investment through intermediary destinations to take advantage of existing BITs. I argue that governments are more likely to participate in BITs when states expect to access groups of capital exporting states through second order links. When selecting BIT partners, states evaluate expected indirect foreign direct investment (FDI) flows by considering characteristics of a potential partner's second order FDI partners. States are thus more likely to participate in BITs when expectations for indirect flows are high. I use a variety of analyses to demonstrate evidence in favor of my hypotheses. I find evidence that indirect flows affect the likelihood of BIT formation and increase dyadic FDI flows. This research provides a novel explanation for BIT formation and contributes to research on indirect capital flows, treaty shopping and BIT formation.

Various factors influence mental well-being, and span individual, social and familial levels. These factors are connected in many ways, forming a complex web of factors and providing pathways for developing programs to improve well-being and for further research. These factors can be studied individually using traditional methods and mapped together to be analyzed holistically from a complex system perspective. This study provides a novel approach using PageRank and social network analysis to understand such maps. The motives are: (1) to realize the most influential factors in such complex networks, (2) to understand factors that influence variations from different network aspects. A previously developed map for children's mental well-being was adopted to evaluate the approach. To achieve our motives, we have developed an approach using PageRank and Social Network Analysis. The results indicate that regardless of the network scale, two key factors called "Quantity and Quality of Relationships" and "Advocacy" can influence children's mental well-being significantly. Moreover, the divergence analysis reveals that one factor, "Recognition/Value Placed on well-being at School" causes a wide range of diffusion throughout the system.

Research into the relationship between ecosystem services and human well-being, including poverty alleviation, has blossomed. However, little is known about who has produced this knowledge, what collaborative patterns and institutional and funding conditions have underpinned it, or what implications these matters may have. To investigate the potential implications of such production for conservation science and practice, we address this by developing a social network analysis of the most prolific writers in the production of knowledge about ecosystem services and poverty alleviation. We show that 70% of these authors are men, most are trained in either the biological sciences or economics and almost none in the humanities. Eighty per cent of authors obtained their PhD from universities in the EU or the USA, and they are currently employed in these regions. The co-authorship network is strongly collaborative, without dominant authors, and with the top 30 most cited scholars being based in the USA and co-authoring frequently. These findings suggest, firstly, that the production of knowledge on ecosystem services and poverty alleviation research has the same geographical and gender biases that characterize knowledge production in other scientific areas and, secondly, that there is an expertise bias that also characterizes other environmental matters. This is despite the fact that the research field of ecosystem services and poverty alleviation, by its nature, requires a multidisciplinary lens. This could be overcome through promoting more extensive collaboration and knowledge co-production.