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The renewable energy product trade is critically important to global economic prospects and its rapid development, making it a key issue in international economics of much interest to scholars. Previous studies have paid attention to bilateral trade, yet we still know little about the patterns of renewable energy product trade and its evolution from the whole industry perspective. Based on bilateral trade data, complex network, as well as ERGM and TERGM, we build global renewable energy trade networks (GRETNs) during 2000–2018 and explore the patterns and determinants. The results show that (1) the GRETNs expand during 2000–2018, characterized by a small-world, reciprocity, degree disassortative, and export volume heterogeneity. (2) The GRETNs form four communities, and the community patterns greatly fluctuate over time. (3) Economies in North America, Europe, and Asia play dominant roles, while the USA, Germany, and China are the cores of the GRETNs. (4) Endogenous structure of reciprocity, structural embeddedness, and out-degree popularity are essential parts of the evolving patterns of GRETNs. Most trade relationships are developed between economies located within the same continent, participating in APEC or WTO, or having similar areas. There is heterophily in GDP and per capita income, and Matthew effects in GDP, urbanization, and industrialization rate. Countries that share a common geographic border, language, religion, or currency, being former colonies of the same colonialists, and having signed regional trade agreements are more likely to trade in renewable energy products. [ABSTRACT FROM AUTHOR]

Academic freedom and global student mobility are both topics high on the scientific and political agenda. However, the relationship between transnational student mobility and academic freedom in national higher education systems has not yet been investigated cross-nationally. This study intends to answer the question on how a countries' level of academic freedom impacts on its' attractiveness as a study destination. We analyse this connection from a network analytic perspective. While our data covers 167 countries as receivers and senders, our network analysis takes the attributes of countries and their relationship to each other into account in order to estimate the net effect of academic freedom ties in the network of global student mobility. We expect global student mobility to be directed from countries with low levels of academic freedom to countries with high levels of academic freedom. At the same time, academic freedom might be an attractive characteristic of a country to retain students in its domestic higher education system. [ABSTRACT FROM AUTHOR]

Knowledge networks in regional clusters are fundamental to support innovation and local development. Within clusters, family firms are key in creating business opportunities and supporting the establishment of inter-organizational networks. Yet, their role within regional clusters for knowledge transfers is still not well understood, especially in comparison with non-family firms. This paper applies Exponential Random Graph Models (ERGMs) to network data collected from the Parabiago cluster, one of the most important Italian footwear clusters, to contribute to a better understanding of the network strategies of family firms. We identify distinct network strategies associated with the cluster firms, accounting for different knowledge exchange types: technological, market, and managerial. In our modelling, we control for firm-level attributes and dyadic-level attributes, such as geographical distance and cognitive proximity between cluster firms. Our results suggest that the proneness of family firms to grow networks is highly robust relative to non-family firm relationships, irrespective of knowledge types being exchanged. Moreover, family firms tend to establish connections with other family firms, showing the presence of homophily in their networking approach; however, non-family firms are rather different, since they do not have the same homophilous approach when it comes to exchange knowledge with other non-family firms. These results indicate that the nature of ownership is driving knowledge exchange differences. This key feature of family-only relationships in clusters may help managers and policymakers in devising more effective and targeted cluster strategies. Plain English Summary: Family firms are key in supporting local development, especially in regional clusters. However, while it is well established that their strategies differ from other (non-family) firms, it is still unclear what is their networking behaviour for supporting knowledge exchange—and thus innovation. This paper provides an empirical overview of this phenomenon, by analyzing an Italian case study: the Parabiago footwear cluster. The results show that (a) family firms are more proactive in establishing network relationships; (b) family firms tend to exchange knowledge with other family firms, while non-family firms do not show the same homophilous approach. Overall, this indicates that policies for clusters need to balance support for distinct business types and recognize the familiness characteristics of regional productive structures. [ABSTRACT FROM AUTHOR]

Actors occupying central positions in networks created from deferential interactions are perceived as high in social status and quality. Status can also be read from increasingly ubiquitous third-party evaluations of actors involved in interactions based on surveys of field participants (e.g., publicly observable ratings). Although evaluations can be reflections of interaction-based positions, I argue the two measures of status can also be discrepant for two reasons: (1) tie formation being a more constrained process than evaluation and (2) differences in determinants of social construction. I propose a creative use of exponential random graph models focused on poorly fit configurations to analyze the divergence between evaluations and statused social networks. I test my framework on a network of PhD exchange relations and peer evaluations. I find that evaluations 'undervalue' both elite and mid-ranked departments relative to their structural positions. I discuss potential explanations and implications of these findings. [ABSTRACT FROM AUTHOR]

As resource shortages and environmental problems keep coming up, economies urgently need renewable energies as the new driving force for development. As one of the representatives of renewable energy, the photovoltaic (PV)'s trade has received much attention from all walks of life. Based on bilateral PV trade data, complex network methods and exponential random graph models (ERGM), this paper constructs global PV trade networks (PVTNs) during 2000–2019, describes detailed evolution features and verifies the influencing factors of the PVTNs. We find that (1) PVTNs have obvious characteristics of the small-world network, accompanied by disassortativity and low reciprocity. (2) Asia, North America, and Europe are the top 3 leading regions in the PVTNs. (3) China is the largest exporter, and the US is the leading recipient. Germany is an essential importer as well as exporter of PVTNs. (4) The formation and evolution of the PVTNs are significantly affected by transitivity, reciprocity, and stability. PV trade is more possible when economy-pairs are WTO members, located on the same continent, or with asymmetrical urbanization rates, industrialization rates, technological level or environmental supervision strength. Specifically, economies with higher industrialization rates, technological levels, stricter environmental regulations or lower urbanization rates are more likely to import PV. Economies with higher economic development, larger area, and greater trade openness are more inclined to trade PV. Besides, economic partners that share a religion or language, have common historical colonial ties or geographic borders or sign regional trade agreements are more likely to trade PV. [ABSTRACT FROM AUTHOR]

The exponential random graph model (ERGM) is an effective approach for testing the dynamic and local processes of a network. This paper explores the structure of stakeholders' partnerships in a tourism waste management network using high-order dependency ERGMs based on relational data obtained from a field survey in Motuo County, China. The results reveal that (1) the network has many edges, indicating a tight network; (2) the geometrically weighted edge distribution shows a high transitive effect of the network; (3) the structural effect is more significant than the attribute effect; (4) there is a good agreement between the simulation results and observations, suggesting a tourism waste network with close connections and collaborative division of labor. These findings indicate that different groups of stakeholders have been extensively involved in tourism waste management in Motuo County. The edgewise shared partners formed by stakeholders of different groups increase the information transmission efficiency of the network. The results have implications for tourism waste management, specifically for promoting sustainability transitions via network governance. [ABSTRACT FROM AUTHOR]

Policy actors address complex environmental problems by engaging in multiple and often interdependent policy issues. Policy issue interdependencies imply that efforts by actors to address separate policy issues can either reinforce ('win–win') or counteract ('trade-off') each other. Thus, if interdependent issues are managed in isolation instead of being coordinated, the most effective and well-balanced solution to the underlying problem might never be realised. This study asks if reinforcing and counteracting interdependencies have different impacts on perception and collaboration. Our empirical study of collaborative water governance in the Norrström basin, Sweden, shows that policy actors often avoid collaborating when the policy issues exhibit reinforcing interdependencies. Our evidence indicates a perceived infeasibility of acting on reinforcing interdependencies. We also find that actors do not consider counteracting interdependencies ('trade-offs') at all when they engage in collaboration. Further, even though actors were aware of counteracting and reinforcing interdependencies, our analyses suggest they might be less aware of the former. These findings illustrate that actors either avoid each other due to policy issue interdependencies or, at best, ignore existing interdependencies when engaging in collaboration. Our study highlights the importance of problem perception in accomplishing integrated solutions to complex environmental problems, and of how understandings of different types of interdependencies shape collaboration in environmental governance. [ABSTRACT FROM AUTHOR]

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The purpose of the present paper is to investigate the formation mechanism of big data technology cooperation networks by considering the combined effect of three key factors, i.e., the individual characteristics, relationship characteristics, and cooperation characteristics of research and development (R&D) entities. The utilized research data come from Chinese big data technology cooperation patents granted during the years 2009–2018. In this paper, an exponential random graph model is applied to study the impact of different indicators on a big data technology cooperation network. The results show that R&D capability and structural holes in the individual characteristics of R&D entities have negative impacts on the formation of big data technology cooperation networks. In contrast, R&D entities with a high degree centrality are beneficial to the development of big data technology cooperation networks. Regarding relationship characteristics, a trend of geographical homogeneity is obvious in the formation process of the examined big data technology cooperation network, while the effect of organisational homogeneity is nonsignificant. In terms of cooperation characteristics, the network tends to facilitate convergent cooperation and transitive cooperation rather than intermediary cooperation. The present results provide a scientific reference for entities working to build effective cooperation relationships and promote the sustainable development of big data technology. [ABSTRACT FROM AUTHOR]

This paper examines the role of complex network patterns in determining international trade. The author proposes two empirical approaches to better identify the influences that the full structure of the trade network has on individual bilateral flows. The first uses gravity models that incorporate novel network covariates. The second uses exponential random graph models (ERGMs) that analyze trade from a network perspective. Estimates from both types of models provide strong evidence that network dependencies are influential determinants at both the intensive and extensive margin. Direct comparisons of the two approaches indicate that each can outperform the other at capturing and replicating certain types of network features. These results indicate that complex network patterns are an important determinant of trade, that gravity models can capture much of this dependency even if not explicitly controlled for, and that empirical network models such as ERGMs can be valuable tools for better capturing certain network patterns. [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]

In sustainability science, interdisciplinarity, i.e., the integration of perspectives from different disciplines, is built collectively from interactions among researchers of various disciplines ("collective interdisciplinarity") but also results from the fact that researchers have backgrounds in multiple disciplines ("individual interdisciplinarity"). We applied social network analysis tools to analyze how individual interdisciplinarity influences collective interdisciplinarity, using the case of a forest sustainability science group. We hypothesized that researchers with higher individual interdisciplinarity had more interdisciplinary interactions and were interdisciplinary brokers within the group. We first analyzed individual interdisciplinarity using a bipartite network of researchers and disciplines. We then analyzed networks of management, research, and publication interactions among researchers in the research group. This showed how disciplines influenced interactions and how researchers contributed to interdisciplinary interactions and brokerage. Results of the first analysis identified large disciplinary communities in the center of the bipartite network, whereas smaller ones were more distant. The second analysis highlighted disciplinary homophily in interaction networks, as two researchers interacted more if they were from the same disciplinary community. Results also showed that the interactions among researchers were structured not only by disciplinary homophily, but also by other forms of homophily related to location or region of work. The key brokers of interactions across disciplinary communities were distributed across several communities, showing that brokerage was not controlled by the large, dominant communities. Analysis of correlations between individual interdisciplinarity and contributions to collective interdisciplinarity did not support our hypothesis but rather hinted at the alternative hypothesis that researchers with high individual interdisciplinarity interacted less with other disciplinary communities. [ABSTRACT FROM AUTHOR]

Multi-layer networks arise when more than one type of relation is observed on a common set of actors. Modeling such networks within the exponential-family random graph (ERG) framework has been previously limited to special cases and, in particular, to dependence arising from just two layers. Extensions to ERGMs are introduced to address these limitations: Conway–Maxwell–Binomial distribution to model the marginal dependence among multiple layers; a "layer logic" language to translate familiar ERGM effects to substantively meaningful interactions of observed layers; and nondegenerate triadic and degree effects. The developments are demonstrated on two previously published datasets. [ABSTRACT FROM AUTHOR]

The frequency and severity of natural hazards are predicted to increase with climate change. Collaboration among actors across scales and organizational boundaries is essential to address this escalation. Pre-existing social networks are generally considered a catalyst enabling actors to more quickly address collective action problems. However, empirically derived knowledge about if, how, and why pre-established social networks facilitate effective collaborations in addressing natural hazards is scarce. We use survey data from crisis responders of large-scale wildfires in Sweden and Canada to investigate factors that shape actors' (i) ability and willingness to form new social ties with other actors and (ii) propensity to "activate" pre-existing social ties. Our results show that many new social ties were established in both events, but also that pre-existing ties comprised a considerable proportion (54–82%) of all ties in use. Using exponential random graph models for temporal networks, we demonstrate that two actors that are working with or have previously worked with a common third actor are more likely to activate pre-existing social ties. Further, new social ties tend to be formed around a few central actors, whereas the opposite seems to apply for the activation of pre-existing ties. The extent to which actors consider others' organizational affiliation, formal role, previous experience, and level of professionalization differs between the cases. We suggest these tie formation and activation differences can be attributed to diverging organizational contexts varying in their reliance upon self-organizing versus command-and-control approaches. [ABSTRACT FROM AUTHOR]

Much research on higher education has discussed the positional competition induced by global rankings and the complementary concept of 'world-class' universities. This paper investigates the network of social media communication between globally ranked universities. Specifically, it examines whether universities seek to preserve and reproduce status by selectively forming associations with highly ranked institutions. It uses social network analysis and exponential random graph models to investigate data on interactions through the popular social media website Twitter.com. Findings show that social media communications are significantly related to global rankings, but that the size of this effect is quite small. Instead, structural relationships within the network and geographical location appear to have more influence on network structure. These results suggest a need to critically reassess the category of 'world-class' universities and the role of global rankings in global higher education. [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]

This paper explores the roles of acute infection and concurrent partnerships in HIV transmission dynamics among young adults in Zimbabwe using realistic representations of the partnership network and all published estimates of stage-specific infectivity. We use dynamic exponential random graph models to estimate partnership network parameters from an empirical study of sexual behavior and drive a stochastic simulation of HIV transmission through this dynamic network. Our simulated networks match observed frequencies and durations of short- and long-term partnerships, with concurrency patterns specific to gender and partnership type. Our findings suggest that, at current behavior levels, the epidemic cannot be sustained in this population without both concurrency and acute infection; removing either brings transmission below the threshold for persistence. With both present, we estimate 20-25% of transmissions stem from acute-stage infections, 30-50% from chronic-stage, and 30-45% from AIDS-stage. The impact of acute infection is strongly moderated by concurrency. Reducing this impact by reducing concurrency could potentially end the current HIV epidemic in Zimbabwe. [ABSTRACT FROM AUTHOR]

Exponential random graph modeling (ERGM) is used here to test hypotheses derived from human behavioral ecology about the adaptive nature of human food sharing. Respondents in all ( n = 317) households in the fishing and sea-hunting village of Lamalera, Indonesia, were asked to name those households to whom they had more frequently given (and from whom they had more frequently received) food during the preceding sea-hunting season. The responses were used to construct a social network of between-household food-sharing relationships in the village. The results show that kinship, proximity, and reciprocal sharing all strongly increase the probability of giving food to a household. The effects of kinship and distance are relatively independent of each other, although reciprocity is more common among residentially and genealogically close households. The results show support for reciprocal altruism as a motivation for food sharing, while kinship and distance appear to be important partner-choice criteria. [ABSTRACT FROM AUTHOR]