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In light of the crucial role of collaborative R&D in advancing technology within the new energy vehicle industry, this study seeks to explore ways to overcome the barriers to technological innovation by establishing an effective collaborative innovation network. Utilizing joint patent-authorized data from China's new energy vehicles between 2005 and 2019, the collaborative innovation network was developed, and the Exponential Random Graph Model (ERGM) was employed to analyze its formation and evolution mechanisms. The results indicate that the network has undergone significant expansion, closely linked to strong national policy support and the active involvement of innovation participants. The network exhibits effects of expansion, transfer, and closure. External attribute analysis revealed the Matthew effect and geographical compatibility effect and found that organizational compatibility tends to foster complementary cooperation. The findings offer insights into optimizing collaborative innovation networks in the NEVs industry and suggest strategies for policymakers and industry players to promote collaborative innovation. [ABSTRACT FROM AUTHOR]

Information exchange is critical to the functionality of governance networks. Traditionally, it has been argued that actors within governance networks tend to engage in information exchange with others who share similar beliefs and motivations, as these are deemed catalysts for achieving collective objectives. An alternative viewpoint posits that actors may prioritize strategies aimed at minimizing transaction costs and maximizing returns when selecting their partners. This paper proposes that information exchange predominantly occurs with partners who are easily accessible (i.e., where transaction costs are low) and with partners who are perceived as influential (i.e., where benefits are high). To investigate these alternative propositions, we examine three distinct opportunity structures that actors may utilize, which are based on their preferences for (1) partners with similar participatory motivations, (2) partners who co‐participate in institutional committees, and (3) those perceived as influential. We empirically test these opportunity structures using unique survey data gathered from 10 anti‐corruption multi‐stakeholder partnerships within the public infrastructure domain in countries of Latin America, Africa, and Eurasia. Results from Exponential Random Graph Models suggest that shared participatory motivations do not significantly impact information exchange within our context, whereas the perceived influence of a partner emerges as a critical predictor. In addition, co‐participation in institutional committees significantly facilitates information dissemination, particularly when those committees involve discussions on deliberating about strategies to communicate findings on public‐sector infrastructure discrepancies and formulating recommendations to governments on transparency and accountability. These findings prompt discussions on four network management strategies aimed at restructuring networks and fostering stakeholder involvement and inclusivity. [ABSTRACT FROM AUTHOR]

Ecological interactions among benthic communities are crucial for shaping marine ecosystems. Understanding these interactions is essential for predicting how ecosystems will respond to environmental changes, invasive species, and conservation management. However, determining the prevalence of species interactions at the community scale is challenging. To overcome this challenge, we employ tools from social network analysis, specifically exponential random graph modelling (ERGM). Our approach explores the relationships among animal and plant organisms within their neighbourhoods. Inspired by companion planting in agriculture, we use spatiotemporal co-occurrence as a measure of mixed species interaction. In other words, the variety of community interactions based on co-occurrence defines what we call 'co-occurrence social diversity'. Our objective is to use ERGM to quantify the proportion of interactions at both the simple paired level and the more complex triangle level, enabling us to measure and compare co-occurrence social diversity. Applying our approach to the Spanish coastal zone across eight sites, five depths, and sunlit/shaded aspects, we discover that 80% of sessile communities, consisting of over a hundred species, exhibit co-occurrence social diversity, with 5% of species consistently forming associations with other species. These organism-level interactions probably have a significant impact on the overall character of the site. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'. [ABSTRACT FROM AUTHOR]

This article explores the role of macro-level factors, specifically political opportunity structures (POS), in shaping interorganizational network tie formation and persistence. Grounded in the POS literature and the multitheoretical multilevel (MTML) framework—specifically using the theories of resource dependency and collective action—we examine how changes in POS are associated with network structures in Malaysian civil society. Data were collected in two phases, reflecting different political contexts. Although the network never evolved into a decentralized or centralized structure, the most central organizations remained consistent and seemed to have been active in bringing others closer to reconfigure toward a more cohesive structure as the POS became more closed. This research contributes to communication network theory by demonstrating how incorporating exogenous environmental factors like POS can advance theory. It offers new insights into operationalizing POS within network analysis and challenges conventional interpretations of centralization in interorganizational relationships. [ABSTRACT FROM AUTHOR]

Collaborative environmental governance (CEG) is increasingly advocated to address the environmental risk issues in the integrated development of urban agglomerations. Constructing an effective CEG network from the perspective of interdependent multilevel network plays a vital role in promoting the environmental governance of urban agglomerations. To investigate the structure characteristics and formation mechanism of CEG network, this paper takes the Yangtze River Delta urban agglomeration as the research area, and employes the social network analysis and Exponential Random Graph Model (ERGM) methods to analyze the CEG network, which consists of the collaborative network of cities, relationship network of topics, and affiliation network connecting cities to topics. Research results show that the CEG level in the Yangtze River Delta urban agglomeration continues to improve, while the CEG network is still not in a tightly connected state. For the collaborative network of cities, it presents the small world characteristics and forms a cooperative trend of “central-subcentral-peripheral city.“For the relationship network of topics, the evolution of environmental governance topics is characterized by “from aspect to point.” For the affiliation network connecting cities to topics, as the diversity of environmental governance topics increases among cities, cities within the Yangtze River Delta urban agglomeration tend to share the similar topics. In addition, the interactive triangular structures, star structures, open triangular structures and closed triangular structures in the network can promote the formation of new cooperative relationships in CEG network.

Abstract The analysis of network data has become an increasingly prominent and demanding field across multiple research fields including data science, health, and social sciences, requiring the development of robust models and efficient computational methods. One well-established and widely employed modeling approach for network data is the Exponential Random Graph Model (ERGM). Despite its popularity, there is a recognized necessity for further advancements to enhance its flexibility and variable selection capabilities. To address this need, we propose a novel hierarchical Bayesian adaptive lasso model (BALERGM), which builds upon the foundations of the ERGM. The BALERGM leverages the strengths of the ERGM and incorporates the flexible adaptive lasso technique, thereby facilitating effective variable selection and tackling the inherent challenges posed by high-dimensional network data. The model improvements have been assessed through the analysis of simulated data, as well as two authentic datasets. These datasets encompassed friendship networks and a respondent-driven sampling dataset on active and healthy lifestyle awareness programs.

Transparent and proactive emergency communication plays a crucial role in effectively responding to public crises. The effectiveness of emergency management throughout a crisis event depends on the organizations the local government collaborates with in information release. This study reviewed the institutional basis of Chinese emergency information release and explored the mechanism of collaborative networks in press conferences through the Exponential Random Graph Model (ERGM). The findings suggest that (1) local governments in China are gradually forming the whole-of-society pattern of emergency information release by establishing network structures with multiple triangulations across different types of organizations, irrespective of their jurisdictional level; (2) while an emergency response plan defines the responsibilities of certain organizations, in practice, network actors extend beyond these designations; (3) past collaborations have a lasting impact on subsequent ones, and the key participants in emergency response tend to create path dependence through long-term collaboration. [ABSTRACT FROM AUTHOR]

Social network analysis can be a powerful tool to better understand the social context of terrorist activities, and it may also offer potential leads for agencies to intervene. Our access to Dutch police information allows us to analyse the relational features of two networks that include actors who planned acts of terrorism and were active in the dissemination of a Salafi-Jihadi interpretation of Islam (n = 57; n = 26). Based on a mixed-method approach that combines qualitative and more formal statistical analysis (exponential random graph models), we analyse the structural characteristics of these networks, individual positions and the extent to which radical leaders, pre-existing family and friendship ties and radicalizing settings affect actors to form ties. We find that both networks resemble a core–periphery structure, with cores formed by a densely interconnected group of actors who frequently meet in radicalizing settings. Based on our findings, we discuss the potential effects of preventive and repressive measures developed within the Dutch counterterrorism framework. [ABSTRACT FROM AUTHOR]

In light of the crucial role of collaborative R&D in advancing technology within the new energy vehicle industry, this study seeks to explore ways to overcome the barriers to technological innovation by establishing an effective collaborative innovation network. Utilizing joint patent-authorized data from China’s new energy vehicles between 2005 and 2019, the collaborative innovation network was developed, and the Exponential Random Graph Model (ERGM) was employed to analyze its formation and evolution mechanisms. The results indicate that the network has undergone significant expansion, closely linked to strong national policy support and the active involvement of innovation participants. The network exhibits effects of expansion, transfer, and closure. External attribute analysis revealed the Matthew effect and geographical compatibility effect and found that organizational compatibility tends to foster complementary cooperation. The findings offer insights into optimizing collaborative innovation networks in the NEVs industry and suggest strategies for policymakers and industry players to promote collaborative innovation.

• Explored the impact of environmental policies on nuclear trade within the EU. • Uncovered connection between environmental policies and nuclear trade relationships. • Countries pursuing environmental policies rely less on nuclear-related imports. • Nuclear trade rises as EU environmental policies align. • Environmentally friendly exporting countries shape the nuclear trade network evolution. This study explores the impact of environmental policies on nuclear trade between European Union (EU) countries. The primary research question revolves around understanding the reasons behind and the mechanisms through which nuclear trade relationships evolve. Our analysis uncovers a compelling connection between the degree of alignment in environmental policies among EU states and the emergence of nuclear trade cooperation. Furthermore, it indicates that countries actively pursuing environmental policies tend to have a reduced dependency on imports of nuclear-related goods. Notably, as environmental policies across the EU converge and become more similar, there is a noticeable inclination to engage in nuclear trade activities. Additionally, our research highlights the significant role played by exporting countries that have strong commitments to environmentally friendly policies in shaping the evolution of the nuclear trade network over time. Moreover, we observe that economic factors have a positive impact on the development of this intricate trade network. [ABSTRACT FROM AUTHOR]

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]

The Exponential Random Graph family of models (ERGM) is a powerful tool for social science research as it allows for the simultaneous modeling of endogenous network characteristics and exogenous variables such as gender, age, and socioeconomic status. However, a major limitation of ERGM is that it is mainly used for descriptive analysis of a single network. This paper examines two methods for estimating multiple networks: hierarchical and integrated. We contrast the two approaches, evaluate their accuracy and discuss the advantages and drawbacks of each. Furthermore, we make recommendations for future researchers on how to proceed with multiple network analysis depending on various factors such as the number of networks and the hierarchical structure of the data. This research is important as it highlights the need for the analysis of multiple networks in order to gain a more comprehensive understanding of social phenomena and the potential for new discoveries. • Analyses of social networks gain popularity in social sciences. • No clear "best"' method for multiple network analysis. analysis. • MC simulation design shows method advantages and drawbacks. • Paper offers recommendations for researchers with multiple networks. • Multiple network analysis yields more robust results. [ABSTRACT FROM AUTHOR]

Much of the theory of estimation for exponential family models, which include exponential-family random graph models (ERGMs) as a special case, is well-established and maximum likelihood estimates (MLEs) in particular enjoy many desirable properties. However, in the case of many ERGMs, direct calculation of MLEs is impossible and therefore methods for approximating MLEs and/or alternative estimation methods must be employed. Many MLE approximation algorithms require an alternative estimate as a starting point. The maximum pseudo-likelihood estimator (MPLE) is frequently taken as this starting point. Here, we discuss a potentially large class of such alternatives based on the fact that, unlike the MLE, the MPLE fails to satisfy the so-called "likelihood principle". This means that different networks may have different MPLEs even if they have the same sufficient statistics. We exploit this fact here to search for improved starting values for approximation-based MLE methods. The method we propose has shown its merit in producing an MLE for a network dataset and model that had defied estimation using all other known methods. • Improving MCMC starting values. • Using simulated annealing for finding starting value. • Maximum pseudolikelihood and simulated annealing. • MLE, MPLE, and the likelihood principle. • Exponential family model for networks. [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]

Exponential-family Random Graph models (ERGM) are widely used in social network analysis when modelling data on the relations between actors. ERGMs are typically interpreted as a snapshot of a network at a given point in time or in a final state. The recently proposed Latent Order Logistic model (LOLOG) directly allows for a latent network formation process. We assess the real-world performance of these models when applied to typical networks modelled by researchers. Specifically, we model data from an ensemble of articles in the journal Social Networks with published ERGM fits, and compare the ERGM fit to a comparable LOLOG fit. We demonstrate that the LOLOG models are, in general, in qualitative agreement with the ERGM models, and provide at least as good a model fit. In addition they are typically faster and easier to fit to data, without the tendency for degeneracy that plagues ERGMs. Our results support the general use of LOLOG models in circumstances where ERGMs are considered.

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

Network anomaly detection solutions can analyze a network’s data volume by protocol over time and can detect many kinds of cyberattacks such as exfiltration. We use exponential random graph models (ERGMs) in order to flatten hourly network topological characteristics into a time series, and Autoregressive Moving Average (ARMA) to analyze that time series and to detect potential attacks. In particular, we extend our previous method in not only demonstrating detection over hourly data but also through labeling of nodes and over the HTTP protocol. We demonstrate the effectiveness of our method using real-world data for creating exfiltration scenarios. We highlight how our method has the potential to provide a useful description of what is happening in the network structure and how this can assist cybersecurity analysts in making better decisions in conjunction with existing intrusion detection systems. Finally, we describe some strengths of our method, its accuracy based on the right selection of parameters, as well as its low computational requirements.

The last two decades have seen considerable progress in foundational aspects of statistical network analysis, but the path from theory to application is not straightforward. Two large, heterogeneous samples of small networks of within-household contacts in Belgium were collected using two different but complementary sampling designs: one smaller but with all contacts in each household observed, the other larger and more representative but recording contacts of only one person per household. We wish to combine their strengths to learn the social forces that shape household contact formation and facilitate simulation for prediction of disease spread, while generalising to the population of households in the region. To accomplish this, we describe a flexible framework for specifying multi-network models in the exponential family class and identify the requirements for inference and prediction under this framework to be consistent, identifiable, and generalisable, even when data are incomplete; explore how these requirements may be violated in practice; and develop a suite of quantitative and graphical diagnostics for detecting violations and suggesting improvements to candidate models. We report on the effects of network size, geography, and household roles on household contact patterns (activity, heterogeneity in activity, and triadic closure). for this article are available online. [ABSTRACT FROM AUTHOR]

With the growth of the social economy and technology, innovation networks have emerged as one of the most significant methods for analyzing the evolution of industrial innovation. Yet, there is a shortage of studies analyzing the components that influence network creation. By highly integrating digital technology with the traditional medical industry chain, the smart medical industry has become one of the important sectors of the digital economy. With the advent of internet-based diagnosis and treatment technologies, innovation inside the smart medical industry has taken the form of a network. This study aims to construct an innovation network by organizing and analyzing patent data from China's smart medical industry cooperation, covering the period from 2005 to 2022. The data is sourced from the IncoPat database. The analysis utilizes the Exponential Random Graph Model (ERGM) approach to conduct regression analysis on various factors. These factors include endogenous structural characteristics, node feature variables such as node emergence time and institutional attributes, as well as the distance network and IPC attribute network. By examining the driving mechanism and influence mechanism that influence the innovation network, this study contributes to the smart medical industry research by gaining a better understanding of the current status of innovation network, which can be advantageous for businesses in this field to accurately recognize and actively promote their innovation practices. [ABSTRACT FROM AUTHOR]

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]

It is challenging for a single development subject to complete complex scientific research tasks due to the peculiarities of saline–alkali land. Collaborative innovation and cooperation can help break through the common critical technology R&D problems in the development process by sharing information, technology, knowledge, and resources. However, the process of saline–alkali land development in China is slow and the transformation rate of research results is low. Few articles analyze the current situation and driving mechanism of the collaborative innovation network for saline–alkali land development from the perspective of the innovation chain. This paper first constructs an undirected weighted collaborative innovation network from the upstream, midstream, and downstream levels of technological innovation for saline–alkali land development, analyzing the network's structural characteristics and spatial distribution features. Then uses ERGM to explore the internal and external driving mechanism for network formation from network self‐organization, subject characteristics, and exogenous environmental factors. The results demonstrate that the distribution of the collaborative innovation network is relatively uniform. However, there are also clusters, and the clusters are mostly centered on universities and scientific research institutions. Both the development subjects and clusters present regional features. Centrality and transitivity are crucial to the internal driving mechanism. In the external driving mechanism, the Matthew effect is modest, and the homogeneity effect is considerable; Organizational and technical proximity play a positive role; Geographical and institutional proximity play a blocking role. This study also provides practical enlightenment for encouraging horizontal and vertical collaborative innovation of sustainable development of saline–alkali land. [ABSTRACT FROM AUTHOR]

Network anomaly detection solutions can analyze a network's data volume by protocol over time and can detect many kinds of cyberattacks such as exfiltration. We use exponential random graph models (ERGMs) in order to flatten hourly network topological characteristics into a time series, and Autoregressive Moving Average (ARMA) to analyze that time series and to detect potential attacks. In particular, we extend our previous method in not only demonstrating detection over hourly data but also through labeling of nodes and over the HTTP protocol. We demonstrate the effectiveness of our method using real-world data for creating exfiltration scenarios. We highlight how our method has the potential to provide a useful description of what is happening in the network structure and how this can assist cybersecurity analysts in making better decisions in conjunction with existing intrusion detection systems. Finally, we describe some strengths of our method, its accuracy based on the right selection of parameters, as well as its low computational requirements. [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]

Social network data often contain missing values because of the sensitive nature of the information collected and the dependency among the network actors. As a response, network imputation methods including simple ones constructed from network structural characteristics and more complicated model-based ones have been developed. Although past studies have explored the influence of missing data on social networks and the effectiveness of imputation procedures in many missing data conditions, the current study aims to evaluate a more extensive set of eight network imputation techniques (i.e., null-tie, Reconstruction, Preferential Attachment, Constrained Random Dot Product Graph, Multiple Imputation by Bayesian Exponential Random Graph Models or BERGMs, k-Nearest Neighbors, Random Forest, and Multiple Imputation by Chained Equations) under more practical conditions through comprehensive simulation. A factorial design for missing data conditions is adopted with factors including missing data types, missing data mechanisms, and missing data proportions, which are applied to generated social networks with varying numbers of actors based on 4 different sets of coefficients in ERGMs. Results show that the effectiveness of imputation methods differs by missing data types, missing data mechanisms, the evaluation criteria used, and the complexity of the social networks. More complex methods such as the BERGMs have consistently good performances in recovering missing edges that should have been present. While simpler methods like Reconstruction work better in recovering network statistics when the missing proportion of present edges is low, the BERGMs work better when more present edges are missing. The BERGMs also work well in recovering ERGM coefficients when the networks are complex and the missing data type is actor non-response. In conclusion, researchers analyzing social networks with incomplete data should identify the network structures of interest and the potential missing data types before selecting appropriate imputation methods. [ABSTRACT FROM AUTHOR]

Health status may shape network structure through network dynamics (tie formation and persistence) and direction (sent and received ties), net of typical network processes. We apply Separable Temporal Exponential Random Graph Models (STERGMs) to National Longitudinal Study of Adolescent to Adult Health survey data (n = 1779) to differentiate how health status shapes network sent and received tie formation and persistence. Results indicate that networks are shaped by withdrawal of adolescents experiencing poor health, highlighting the importance of separating distinct and directed processes of friendship formation and persistence when considering how health relates to adolescent social life. • Self-rated health relates to adolescent friendship networks. • Health status may shape making or maintaining sent and received ties. • Withdrawal shapes networks: teens in poor health are less likely to send ties. [ABSTRACT FROM AUTHOR]

Researchers often use pooled exponential random graph models (ERGM) to analyze samples of networks. However, pooled ERGM—here, understood to include both meta-regression and combined estimation on a stacked adjacency matrix—may be biased if there is heterogeneity in the latent error variance ('scaling') of each lower-level model. This study explores the implications of scaling for pooled ERGM analysis. We illustrate that scaling can produce bias in pooled ERGM coefficients that is more severe than in single-network ERGM and we introduce two methods for reducing this bias. Simulations suggest that scaling bias can be large enough to alter conclusions about pooled ERGM coefficient size, significance, and direction, but can be substantially reduced by estimating the marginal effect within a block diagonal or random effects meta-regression framework. We illustrate each method in an empirical example using Add Health data on 15 in-school friendship networks. Results from the application illustrate that many substantive conclusions vary depending on choice of pooling method and interpretational quantity. • Scaling creates novel problems in pooled exponential random graph models that do not exist in the single network case. • Pooled ERGM coefficients can be the incorrect sign, size, and significance when scaling is present. • Two bias reduction methods are introduced. • Block diagonal estimation and random effects meta-regression of average marginal effects decrease scaling bias. • An empirical example on pooled analysis of 15 AddHealth in-school friendship networks is provided. [ABSTRACT FROM AUTHOR]

Understanding the relationships within product co-purchasing is crucial for designing effective cross-selling and recommendation systems in e-commerce. While researchers often detect co-purchase rules based on product attributes, this study explores the influence of consumer behavior preferences and electronic word-of-mouth (eWOM) on co-purchase formation by analyzing the topological network structure of products. Data were collected from a major Chinese e-retailer and analyzed using an exponential random graph model (ERGM) to identify the factors affecting the formation of follow-up purchases between products: the role of topological structure, product category, and online product reviews. The results showed that the co-purchase network was a sparse small-world network, with a product degree of centrality that positively impacted its sales volume within the network, suggesting a concentration effect. Cross-category purchases significantly contribute to the formation of co-purchase relationships, with a differential homophily effect. Positive ratings and review volumes were found to be key factors impacting this co-purchase formation. In addition, a higher inconsistency of positive ratings among products decreases the likelihood of co-purchase. These findings contribute to the literature on eWOM and electronic networks, and have valuable implications for e-commerce managers.

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]

Purpose: Guided by the collective action theory, signaling theory and social identity approach, this study examines backing behavior by individuals who have created projects under CC licenses. Two motivational mechanisms were examined: (1) identification via common interests in the CC space; (2) resource signaling by other users via their diverse project creation experience, funding or commenting activity. Design/methodology/approach: Data were collected from Kickstarter.com. Exponential random graph modeling was used to examine how the two reviewed mechanisms influence the tie formation probability between Creative Commons (CC) project creators and other creators. The analysis was conducted on two subnetworks: one with ties between CC creators; and one with ties from CC creators to non-CC creators. Findings: The study found that CC creators exhibit distinct backing patterns when considering funding other CC creators compared to non-CC users. When considering funding their peer CC creators, CC identity can help them allocate and support perceived in-group members; when considering funding non-CC creators, shared common interests in competitive project categories potentially triggers a competition mindset and makes them hold back when they see potential rivals. Originality/value: This study makes three contributions. First, it draws from multiple theoretical frameworks to investigate unique motivations when crowdfunders take on dual roles of creators and funders and offered implications on how to manage competition and collaboration simultaneously. Second, with network analysis our study not only identifies multiple motivators at work for collective action, but also demonstrates their differential effects in crowdfunding. Third, the integration of multiple theoretical frameworks allows opportunities for theory building. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2020-0166. [ABSTRACT FROM AUTHOR]

The interorganizational relationship communication literature has identified homophily – the tendency for actors to form ties with similar others – as a mechanism predictive of tie formation among organizations in civil society networks. This study examined the connection between homophily and network structures equated with different types of social capital and perceptions of influence. Using survey data gathered from a network of Malaysian civil society organizations (n = 90), exponential random graph models and autologistic actor attribute models were used to test the association between homophily characteristics and the networked social capital positions of bridging, bonding, and gatekeeping. Results showed that bonders and brokers tended to be influenced by homophily, whereas gatekeepers were influenced by heterophily and homophily. Homophily was also associated with the likelihood of CSOs rating each other as more influential on government reform. [ABSTRACT FROM AUTHOR]

Understanding the relationships within product co-purchasing is crucial for designing effective cross-selling and recommendation systems in e-commerce. While researchers often detect co-purchase rules based on product attributes, this study explores the influence of consumer behavior preferences and electronic word-of-mouth (eWOM) on co-purchase formation by analyzing the topological network structure of products. Data were collected from a major Chinese e-retailer and analyzed using an exponential random graph model (ERGM) to identify the factors affecting the formation of follow-up purchases between products: the role of topological structure, product category, and online product reviews. The results showed that the co-purchase network was a sparse small-world network, with a product degree of centrality that positively impacted its sales volume within the network, suggesting a concentration effect. Cross-category purchases significantly contribute to the formation of co-purchase relationships, with a differential homophily effect. Positive ratings and review volumes were found to be key factors impacting this co-purchase formation. In addition, a higher inconsistency of positive ratings among products decreases the likelihood of co-purchase. These findings contribute to the literature on eWOM and electronic networks, and have valuable implications for e-commerce managers. [ABSTRACT FROM AUTHOR]

This paper provides an examination of inter-organizational collaboration in the UK research system. Data are collected on organizational collaboration on projects funded by four key UK research councils: Arts and Humanities Research Council, Economic and Social Research Council, Engineering and Physical Sciences Research Council, and Biotechnology and Biological Sciences Research Council. The organizational partnerships include both academic and nonacademic institutions. A collaboration network is created for each research council, and an exponential random graph model is applied to inform on the mechanisms underpinning collaborative tie formation on research council-funded projects. We find that in the sciences, collaborative patterns are much more hierarchical and concentrated in a small handful of actors compared to the social sciences and humanities projects. Institutions that are members of the elite Russell Group (a set of 24 high-ranking UK universities) are much more likely to be involved in collaborations across research councils. [ABSTRACT FROM AUTHOR]

This study investigates inter- and intracoder reliability, proposing a new approach based on social network analysis (SNA) and exponential random graph models (ERGM). During a recent exit poll, the responses of voters to two open-ended questions were recorded. A coding experiment was conducted where a group of coders coded a sample of text segments. Analyzing the data, we show that the proposed SNA/ERGM method extends significantly our analytical leverage, beyond what popular tools such as Krippendorff's α and Fleiss's κ have to offer. The reliability of coding for individual coders differed significantly for the two questions although they were very similar and the same codebook was used. We conclude that the main advantages of the proposed SNA/ERGM method are the intuitive visualizations and the nuanced measurements. Detailed guidelines are provided for practitioners who would like to use the proposed method in operational settings. [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]

Between October 2021 and April 2022, 317 outbreaks caused by highly pathogenic avian influenza (HPAI) H5N1 viruses were notified in poultry farms in the northeastern Italian regions. The complete genomes of 214 strains were used to estimate the genetic network based on the similarity of the viruses. An exponential random graph model (ERGM) was used to assess the effect of 'at-risk contacts', 'same owners', 'in-bound/out-bound risk windows overlap', 'genetic differences', 'geographic distances', 'same species', and 'poultry company' on the probability of observing a link within the genetic network, which can be interpreted as the potential propagation of the epidemic via lateral spread or a common source of infection. The variables 'same poultry company' (Est. = 0.548, C.I. = [0.179; 0.918]) and 'risk windows overlap' (Est. = 0.339, C.I. = [0.309; 0.368]) were associated with a higher probability of link formation, while the 'genetic differences' (Est. = −0.563, C.I. = [−0.640; −0.486]) and 'geographic distances' (Est. = −0.058, C.I. = [−0.078; −0.038]) indicated a reduced probability. The integration of epidemiological data with genomic analyses allows us to monitor the epidemic evolution and helps to explain the dynamics of lateral spreads casting light on the potential diffusion routes. The 2021–2022 epidemic stresses the need to further strengthen the biosecurity measures, and to encourage the reorganization of the poultry production sector to minimize the impact of future epidemics. [ABSTRACT FROM AUTHOR]

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This paper considers the change-point problem for finite sequences of networks. To avoid the difficulty of computing the normalization coefficient in the models such as Exponential Random Graphical Model (ERGM) and Markov networks, we construct a finite measure space with measure ratio statistics. A new performance measure of detection delay is proposed to detect the changes in distribution of the network data. And under the performance measure we defined, an optimal sequential test is presented. The good performance of the optimal sequential test is illustrated numerically on ERGM and Erdős–Rényi network sequences. [ABSTRACT FROM AUTHOR]

Distinctiveness theory suggests that numeric rarity is correlated with stronger homophily. In this paper, we examine this theory by studying gender homophily in social networks of older adults. We document subjective social networks in multiple long term care settings for older adults over several time points. Homophily for each gender is estimated using exponential random graph models. We find evidence for positive homophily across all networks, and show that it is correlated to the magnitude of the female majority or male minority. Our findings empirically verify distinctiveness theory and could improve interventions to promote tie formation in social networks. • Studied social networks of older adults' long term care settings at various times. • Gender homophily estimated in each network using ERGM to control for other factors. • Homophily correlated with gender distribution, supporting Distinctiveness theory. • No differences found between two types of older adult long term care settings. • Discarded additional alternative explanation of gender homophily. [ABSTRACT FROM AUTHOR]

The significance and influence of U.S. Supreme Court majority opinions derive in large part from opinions' roles as precedents for future opinions. A growing body of literature seeks to understand what drives the use of opinions as precedents through the study of Supreme Court case citation patterns. We raise two limitations of existing work on Supreme Court citations. First, dyadic citations are typically aggregated to the case level before they are analyzed. Second, citations are treated as if they arise independently. We present a methodology for studying citations between Supreme Court opinions at the dyadic level, as a network, that overcomes these limitations. This methodology—the citation exponential random graph model, for which we provide user-friendly software—enables researchers to account for the effects of case characteristics and complex forms of network dependence in citation formation. We then analyze a network that includes all Supreme Court cases decided between 1950 and 2015. We find evidence for dependence processes, including reciprocity, transitivity, and popularity. The dependence effects are as substantively and statistically significant as the effects of exogenous covariates, indicating that models of Supreme Court citations should incorporate both the effects of case characteristics and the structure of past citations. [ABSTRACT FROM AUTHOR]