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Abstract Background Interdisciplinary cooperation among university actors and resulting intersectoral synergies are considered cornerstones in the process of incorporating health promotion practices in everyday university life in order to break down barriers and provide better access to health promotion services. To date, no network of a health-promoting university has been examined regarding the processes underlying tie formation, network emergence, and maintenance. Objectives and methods The goals of this study are to obtain insight into the mechanisms of cooperation between university actors in a health-promoting network and to identify the structural and attributive factors associated with establishing cooperation between actors in the observed network in order to better understand how to build and develop successful networks in the future. For this purpose, a social network analysis was carried out and exponential random graph models were estimated to test corresponding hypotheses. Results The network at hand consists of 33 actors (e.g. University Sports Center, General Student Committee) and shows a flat, non-hierarchical structure. Data reveal that attributed competence predicts cooperation (0.32; p

This study identifies security-related factors affecting the formation of the global arms trade network. This empirical analysis using a quantitative approach includes data from multiple sources (the Global Peace Index, Political Stability Index, Democracy Index, Global Terrorism Index, Fragile State Index, and military expenditure as a percentage of GDP) and multiple states analyzed using the ERGM. Arms trade data related to six attributes of states representing their (in)stability is collected and analyzed for 2012-2018. Our findings are as follows: (1) states with greater internal stability import more arms, which affects the formation of the global arms trade network; (2) states with greater external instability import more arms, which also affects the formation of the global arms trade network. This study makes two academic contributions, as follows. First, we analyze factors that form the global arms trade network from a holistic or systemic perspective. Second, we analyze those factors empirically and statistically from a security perspective. [ABSTRACT FROM AUTHOR]

Background: Interdisciplinary cooperation among university actors and resulting intersectoral synergies are considered cornerstones in the process of incorporating health promotion practices in everyday university life in order to break down barriers and provide better access to health promotion services. To date, no network of a health-promoting university has been examined regarding the processes underlying tie formation, network emergence, and maintenance. Objectives and methods: The goals of this study are to obtain insight into the mechanisms of cooperation between university actors in a health-promoting network and to identify the structural and attributive factors associated with establishing cooperation between actors in the observed network in order to better understand how to build and develop successful networks in the future. For this purpose, a social network analysis was carried out and exponential random graph models were estimated to test corresponding hypotheses. Results: The network at hand consists of 33 actors (e.g. University Sports Center, General Student Committee) and shows a flat, non-hierarchical structure. Data reveal that attributed competence predicts cooperation (0.32; p < 0.05). Significant homophily effects among student actors (1.31; p < 0.05) and among university actors (0.59; p < 0.05) were found. All structural predictors examined were significant (0.22–5.40; p < 0.05) and are therefore essential in determining the likelihood of cooperation between actors involved in the network. Conclusion: The results of this study provide for a better understanding of the mechanisms of cooperation and can be used to further develop the network at hand (e.g. selection of key actors for information dissemination or integration of peripheral actors). In addition, the findings offer starting points for sustained network development at other universities (e.g. significance of network governance form or goal consensus). Knowing the factors that influence the network structure, here the conditions of cooperation, results in opportunities to encourage empowerment among actors. However, the analysis of the network undertaken does not directly bear on the success of the network. [ABSTRACT FROM AUTHOR]

The extent to which a policy actor is perceived as being influential by others can shape their role in a policy process. The interest group literature has examined how the use of advocacy tactics, such as lobbying or media campaigns, contributes to an actor's perceived influence. The policy networks literature, in turn, has found that network ties and occupying certain institutional roles can explain why actors are perceived as influential. When investigating what explains perceptions of influence, interest groups scholars have not accounted for network interdependencies and network scholars have so far not examined the advocacy tactics used by interest groups. This paper addresses the gap at the intersection of these two literatures by investigating the relationship between network ties, institutional roles, advocacy tactics and the presence of influence attribution ties in climate change policy networks. Exponential random graph models are applied to network data collected from the organisations participating in the national climate change policymaking processes in six EU countries that vary by the extent to which they are majoritarian or consensual democracies: Czechia, Finland, Germany, Ireland, Portugal, and Sweden. The results show that network ties and institutional roles are better predictors of influence attribution ties than advocacy tactics and that there is no pattern in the relationship between advocacy tactics and influence attribution ties across different institutional contexts. These findings suggest that because influence is primarily associated with structural factors (network ties and institutional roles) that more established policy actors are likely to have more influence, which may inhibit the need for a significant step change in climate policies. • This paper investigates what determines perceptions of influence in climate change policy networks. • Network ties and institutional roles are better predictors of influence than advocacy tactics. • Advocacy tactics rest on relational assumptions, and as such, can be fruitfully integrated with the policy network approach. [ABSTRACT FROM AUTHOR]

Choice modeling is important in transportation planning, marketing and engineering design, as it can quantify the influence of product attributes and customer demographics on customers' choice behaviors. Consumer studies suggest that customers' choice-making process often consists of two different stages: customers first consider subsets of available products on the market, and then make the final choice from the subsets. As existing preference modeling is mostly focused on the choice stage, there is a need to develop methods for understanding customer preferences at both stages, and investigate how customer preferences change from "consideration" to "choice", and whether such changes will be consistent over time. In this paper, we study customers' consideration and purchase behaviors in China's auto market using multi-year survey datasets. We demonstrate how descriptive network analysis and analytic network models (bipartite Exponential Random Graph Model (ERGM)) capture the change of customers' preferences from the consideration stage to the choice stage in multiple consecutive years. Our results show that factors such as fuel consumption per unit power, car make origin, and place of production influence customers' considerations and final purchase decisions in different ways, and this difference between consideration and purchase is consistent over time. The main contribution of this study is that we validate the two-stage network-based modeling approach and its utility in preference elicitation using multiple-year dataset, which sheds lights on understanding the trend of customers' consideration and choice behaviors across years. Our study also contributes to a refined interpretation of the ERGM results with categorization of continuous variables into ranges, which shows that customer choice decisions may be more qualitatively influenced by product attributes rather than quantitatively. Our approach is generic and thus can be applied to solving broader choice modeling problems, such as the transportation mode selection and the adoption of clean technology (e.g., electric vehicles). [ABSTRACT FROM AUTHOR]

How does international financial aid affect the cooperative behavior of local non-governmental organizations (NGOs)? Can NGOs, while turning global, preserve peer connections with local actors and be engaged in local issues? The civil society literature contains competing perspectives on and reports of how international financial aid may restructure local civic networks. Some scholars argue that international support comes at the expense of local integration as inclusion in global networks takes local NGOs out of the local context, while others find evidence that organizations do not have to face "a forced choice", and may well be integrated both globally and locally. Drawing on this scholarship, we examine two hypotheses on how transnational funding influences cooperation patterns among NGOs. The hierarchy argument states that public entities tend to cooperate with internationally funded NGOs as external contact signals quality and trustworthiness. The segregation argument, on the contrary, suggests financial homophily according to which organizations are more likely to choose peers similar in sources of funding. To test these hypotheses, we apply Exponential Random Graph Models to the data on cooperation among 221 Kazakhstani NGOs. Results support the segregation hypothesis implying that NGOs are likely to have a bias towards similarly funded peers. [ABSTRACT FROM AUTHOR]

Increasing attention is being given to integrating adaptation and mitigation in climate change policies. Policy network analysis is a way to explore connections between adaptation and mitigation, and the opportunities or barriers to effective integration between these two policy subdomains. This study explores climate governance and policy networks by examining collaboration and information flows in national policy processes in Peru, a country with an active climate change policy domain. In contrast to most climate policy network analyses, this study distinguishes adaptation and mitigation subdomains through a multiplex approach. We used ERGM (Exponential Random Graph Models) to explain the existence of information flows and collaborations among 76 key actors in climate change policy in Peru. We identified actors who could connect adaptation and mitigation subdomains. Results show a concentration of influence in national government actors, particularly in the mitigation subdomain, and the isolation of actor groups that matter for policy implementation, such as the private sector or subnational actors. Results highlight the predominance of mitigation over adaptation and the existence of actors well positioned to broker relationships between the subdomains. The top brokers across subdomains were, however, not only actors with high centrality and brokerage roles in the subdomains, but also several 'unusual key players' that were not brokers in any of the two layers separately. Key policy insights National government institutions are central actors in climate change policy networks in Peru, reflecting national ownership of the climate change issue. Private sector organizations and subnational actors in Peru are the least involved in information sharing and collaboration on climate change. Actors from different levels and sectors are active in both adaptation and mitigation, which is good for climate policy integration. Actors with the capacity to bridge the two policy subdomains are not necessarily central to each subdomain but may be actors that close structural holes between subdomains. [ABSTRACT FROM AUTHOR]

The challenge in evaluating China's foreign aid has always been the unavailability of reliable data sets. This study constitutes the first analysis of the AidData data set from a communication network perspective. It examines China's development aid to Africa in the ICT sector from 2000 to 2014. Combining data mapping, network modeling, and regression, it uncovers general trends of aid allocation, central players, and collaboration patterns among aid agencies. The results demonstrate the variability in the distribution of China's foreign assistance to 44 African countries. In particular, African countries with less population, worse economic development, but higher oil rents are more likely to receive ICT aid from China. This study also finds that aid implementation is less likely to occur through collaboration within the same sector or between state-owned enterprises (SOEs) and private companies. This research reveals nuanced geometries of aid with "Chinese characteristics" that move beyond the extractive "Angola model" or the mutual benefits model. These findings provide implications on how Chinese telecommunication companies are shaping Africa's digital future. [ABSTRACT FROM AUTHOR]

Since few states are able to produce all of their own military hardware, a majority of countries' military systems rely on weapon imports. The structure of the international defense technology exchange network remains an important puzzle to understand, along with the factors that drive its evolution. Drawing on a political economy model of arms supply, we propose a new network-oriented explanation for the worldwide transactions of major conventional weapons in the period after World War II. Using temporal exponential random graph models, our dynamic approach illustrates how network dependencies and the relative weighting of economic versus security considerations vary over time. One of our major results is to demonstrate how security considerations started regaining importance over economic ones after 2001. Additionally, our model exhibits strong out-of-sample predictive performance, with network dependencies contributing to model improvement especially after the Cold War. [ABSTRACT FROM AUTHOR]

Understanding human and animal mobility patterns is a key to predict local and global disease spread. We analysed the nomad herds' movement network in a pilot area of northern Senegal and used exponential random graph models (ERGM) to investigate the reasons behind these movements. We interviewed 132 nomadic herders to collect information about nomad herd structures, movements, and reasons for taking specific routes or gathering in certain areas. We constructed a spatially explicit network with villages as the nodes and nomad herds' movements as the connecting edges. The final ERGM showed that node and edge attributes such as presence of cattle in the herd (odds ratio = 12, CI: 5.3, 27.3), morbidity (odds ratio = 3.6, CI: 2.3, 5.7), and lack of water (odds ratio = 2, CI: 1.3, 3.1) were important predictors of nomad herds' movements. This study not only provides valuable information for monitoring important livestock diseases such as Rift Valley Fever in Senegal, but also helps implement outreach, education, and intervention programs for other emerging and endemic diseases affecting nomadic herds. [ABSTRACT FROM AUTHOR]

This study applied collective action theories and network theories to examine the information sharing patterns among Twitter users to obtain sociopolitical legitimacy of their collective goal. The role of Twitter in facilitating private–public boundary crossing was defined in relation to main challenges of collective action. The hypotheses and research question were examined using Twitter data collected from an online campaign, which was created to bring about the release of a detained Syrian activist. Network analysis results showed significant geographic homophily effect, that is, participants located in the same region tended to share information with each other. In addition, the results indicated that more influential Twitter users tended to connect with less influential users to help spread information on the movement. Further content analysis showed that to better mobilize potential collective action participants, Twitter users utilized strategies to draw attention from citizen news media organizations, nonprofit advocacy organizations, public figures, and corporations. [ABSTRACT FROM AUTHOR]

Background: Cryptomarkets, or illegal anonymizing online platforms that facilitate drug trade, have been analyzed in a rapidly growing body of research. Previous research has found that, despite increased risks, cryptomarket sellers are often willing to ship illegal drugs internationally. There is little to no information, however, about the extent to which uncertainty and risk related to geographic constraints shapes buyers' behavior and, in turn, the structure of the global online drug trade network. In this paper, we analyze the structure of a complete cryptomarket trade network with a focus on the role of geographic clustering of buyers and sellers.Methods: We use publicly available crawls of the cryptomarket Abraxas, encompassing market transactions between 463 sellers and 3542 buyers of drugs in 2015. We use descriptive social network analysis and Exponential Random Graph Models (ERGM) to analyze the structure of the trade network.Results: The structure of the online drug trade network is primarily shaped by geographical boundaries. Buyers are more likely to buy from multiple sellers within a single country, and avoid buying from sellers in different countries, which leads to strong geographic clustering. The effect is especially strong between continents and weaker for countries within Europe. A small fraction of buyers (10%) account for more than a half of all drug purchases, while most buyers only buy once.Conclusion: Online drug trade networks might still be heavily shaped by offline (geographic) constraints, despite their ability to provide access for end-users to large international supply. Cryptomarkets might be more "localized" and less international than thought before. We discuss potential explanations for such geographical clustering and implications of the findings. [ABSTRACT FROM AUTHOR]

Multistakeholder issue networks have been characterized as power-free, egalitarian forms of corporate–civil society engagement. Using a communication-centered conceptualization of power, our study finds that potential sources of power subtly manifest through communication and interaction patterns in multistakeholder issue networks. Our results indicate that organizations’ institutional status and resources are significant predictors of network power. [ABSTRACT FROM AUTHOR]

Data-driven and integrated urban water management have been proposed to reduce surface water pollution in light of climate change and urbanization impacts. Besides technological innovation, data-driven and integrated management require information exchange among many actors, e.g., operators, engineers, or authorities. With the aim of achieving a more profound understanding of socio-technical infrastructures, such as urban water systems, I draw on the approach of socio-technical networks to study actors and infrastructure elements as well as multiple relations in-between. In this article, I investigate whether underlying socio-technical dependencies influence social interactions such as information exchange. More specifically related to data-driven and integrated management, I analyze potential challenges, such as organizational fragmentation, data access, and diverging perceptions. Based on empirical data from three case studies in Switzerland, I provide inferential results obtained from fitting exponential random graph models. Findings showed that actors' relatedness to infrastructure elements affects their information exchange. Among the cases, the presence of the three challenges varied and is potentially contingent upon system size, organizational form, or progress in terms of data-driven and integrated management. Thus, incorporating a socio-technical perspective on social actors and infrastructure elements could help to improve policy design and implementation aiming to achieve more sustainable cities. [ABSTRACT FROM AUTHOR]

Long-term changes in visibility patterns between urban settlements are considered an important factor for understanding Iron Age II settlement locations in Southern Spain. From some settlements, the surrounding landscape and other settlements could be visually controlled, and some settlements are argued to be intervisible to allow for communication through visual signals. However, the study of how these visibility patterns changed in the subsequent Roman period in this region is largely ignored. In this paper, we argue that visibility might still have structured interactions between communities in Roman times and should not be dismissed out of hand merely because more and other data sources are available as compared to the Iron Age. However, the way in which it affected human behaviour might have been different in Roman times as compared to the Iron Age. We argue that simulating archaeologists' hypotheses about the emergence of inter-settlement visibility is a promising way of understanding such differences. To do this, we use exponential random graph modelling (ERGM), a statistical network simulation modelling technique that allows us to simulate hypotheses about the emergence and long-term change of visibility networks. We combine this approach with an exploratory analysis of the observed visibility networks between identified urban settlements, which will reveal similarities and differences in the changing patterns of visibility networks through time. The results of the ERGMs are then compared with the changes in the observed network structure. We conclude that our knowledge of the changes from the Iron Age II to the Roman settlement pattern suggests only gradual changes in the role of visibility in structuring inter-settlement interactions, possibly followed by a disintegration of the visibility network after the Roman Early Imperial period. [ABSTRACT FROM AUTHOR]

Archaeological network analysts often represent archaeological data as static networks and explore their structure. However, most networks changed through time and static network representations do not allow archaeologists to test assumptions about the dynamic processes driving this change. The study of visibility networks in archaeology is a good example of this. Archaeologists propose hypotheses of the role of lines of sight between settlements, which imply dynamic processes for the establishment of the observed visibility networks. However, commonly used methods do not allow us to evaluate these hypotheses. In this paper we introduce exponential random graph modelling (ERGM) as a method for bridging static and dynamic approaches to interpreting visibility networks. This method offers a number of advantages: (1) it explicitly addresses the assumptions inherent in visibility network creation about what relationships between nodes mean and the types of processes they allow for; (2) it allows one to investigate the range of network structures that these assumptions give rise to; and (3) it explores the dynamic processes that might have led to observed networks. This method is used to evaluate hypotheses of the role of lines of sight in facilitating visual control and communication during the later Iron Age in Southern Spain. This study shows that ERGMs can be used as a reflective technique to evaluate competing hypotheses, and that ERGM results subsequently require more contextualised evaluation. Future work on ERGMs should focus on incorporating geographical constraints to further enhance its potential for studying visibility networks. [ABSTRACT FROM AUTHOR]