Exponential Random Graph Models Research Articles

Research on scientific knowledge evolution patterns based on ego-centered fine-granularity citation network

A deep comprehension of the development of scientific knowledge is necessary to properly appreciate technological advancement and the process of knowledge innovation. However, fine-grained citation networks are less frequently utilized to characterize the evolution of scientific knowledge. To bridge this gap, we first constructed the fine-grained citation networks based on the PubMed and Web of Science (WOS) databases, then extracted the ego-centered networks of scientific knowledge, and finally employed Exponential Random Graph Models (ERGMs) to analyze the factors influencing the formation of Ego-centered Fine-granularity Citation Networks (EFCNs), taking into account the endogenous network structure and exogenous knowledge attribute variables. The results reveal that both types of variables play pivotal roles in the evolution of scientific knowledge. Furthermore, we found that (1) the in-degree and the out-degree centrality have a positive effect on knowledge evolution, respectively, in the 98.5 % and 99.9 % sample networks, while the clustering coefficient only has a positive effect on the edge formation of the 6.8 % sample network at the 0.05 significance level. (2) The citation behavior and domain impact of authors positively influence the scientific knowledge evolution, respectively, in the 63.2 % and 78.3 % sample networks. (3) There is a tendency to form citation relationships between scientific knowledge units of similar age in 67.1 % of the sample networks. (4) There is a greater possibility of developing a citation relationship between scientific knowledge with the same journal impact rank and knowledge type. Our findings indicate that the evolution of scientific knowledge is influenced not only by the process of scholarly communication but also by self-organizing mechanisms at the fine granularity level.

Approximate inference for longitudinal mechanistic HIV contact network

Network models are increasingly used to study infectious disease spread. Exponential Random Graph models have a history in this area, with scalable inference methods now available. An alternative approach uses mechanistic network models. Mechanistic network models directly capture individual behaviors, making them suitable for studying sexually transmitted diseases. Combining mechanistic models with Approximate Bayesian Computation allows flexible modeling using domain-specific interaction rules among agents, avoiding network model oversimplifications. These models are ideal for longitudinal settings as they explicitly incorporate network evolution over time. We implemented a discrete-time version of a previously published continuous-time model of evolving contact networks for men who have sex with men and proposed an ABC-based approximate inference scheme for it. As expected, we found that a two-wave longitudinal study design improves the accuracy of inference compared to a cross-sectional design. However, the gains in precision in collecting data twice, up to 18%, depend on the spacing of the two waves and are sensitive to the choice of summary statistics. In addition to methodological developments, our results inform the design of future longitudinal network studies in sexually transmitted diseases, specifically in terms of what data to collect from participants and when to do so.

Invisible Iterations: How Formal and Informal Organization Shape Knowledge Networks for Coordination

AbstractThis study takes a network approach to investigate coordination among knowledge workers as grounded in both formal and informal organization. We first derive hypotheses regarding patterns of knowledge‐sharing relationships by which workers pass on and exchange tacit and codified knowledge within and across organizational hierarchies to address the challenges that underpin contemporary knowledge work. We use survey data and apply exponential random graph models to test our hypotheses. We then extend the quantitative network analysis with insights from qualitative interviews and demonstrate that the identified knowledge‐sharing patterns are the micro‐foundational traces of collective coordination resulting from two underlying coordination mechanisms which we label ‘invisible iterations’ and ‘bringing in the big guns’. These mechanisms and, by extension, the associated knowledge‐sharing patterns enable knowledge workers to perform in a setting that is characterized by complexity, uncertainty and ambiguity. Our research contributes to theory on the interplay between formal and informal organization for coordination by showing how self‐directed, informal action is supported by the formal organizational hierarchy. In doing so, it also extends understanding of the role that hierarchy plays for knowledge‐intensive work. Finally, it establishes the collective need to coordinate work as a previously overlooked driver of knowledge network relationships and network patterns.

Hierarchical Bayesian adaptive lasso methods on exponential random graph models

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.

Policy influence and influencers online and off

AbstractSocial media is an important arena for policy contestation. Although online social media debates can yield notable power over political processes offline, little research has examined the relationship between policy actors' influence in offline policymaking and social media policy arenas. We explore the relationship between four types of influence: reputational and formal‐institutional influence offline, and broadcasting and boosting influence online. We ask (1) are influential policy actors better able than others to broadcast their own messages on social media? and (2) are they better able than others to boost the broadcasting influence of other policy actors in social media? Using exponential random graph models on survey and Twitter data from the Finnish climate policy domain, we find that actors with high reputational influence in offline policymaking are also influential online, when measuring influence as the ability to broadcast one's own message. The pattern does not hold for those with formal‐institutional influence offline. Additionally, offline influence does not translate to the ability to further shape online influence by making boosting other actors' visibility. Our results suggest that although online influence partially corresponds to influence in policymaking, influence varies across arenas of policy contestation.

Transboundary cooperation in Arctic climate change governance under geopolitical tensions

Political conflicts or geopolitical tensions can create uncertainty in addressing climate change and environmental management in the Arctic. Dissecting how actors interact with each other and form networks is important for understanding ecological and environmental management challenges during geopolitical tensions, as well as promoting better governance. We construct transboundary networks for Arctic climate change governance (ACCG) from 2013 to 2021 based on the Global Database of Events, Language, and Tone (GDELT). Further, we used network descriptive statistical analysis and Temporal Exponential Random Graph Models (TERGM) to explore the structure of ACCG networks and the key factors influencing cooperation formation. The findings suggest that the overall cooperation density of the ACCG is low, and the dominant position of core actors is continuously strengthening. Non-state actors are less likely to be seen as partners and their participation depends largely on cooperation with states. The results also show that actors with similar stances and problem exposure are more likely to cooperate, but those exposed to high latitudes often choose not to cooperate; first-comers are more likely to perceive as cooperating yet they are inclined to establish internal cooperation. Additionally, two geographically proximate actors are more likely to cooperate. This indicates that under geopolitical tensions, the ACCG faces challenges not only due to the limited capacity of non-state actors to perform transboundary functions but also because the cooperation mechanisms are influenced by regional political logic. Accordingly, we further suggest policy recommendations from developing binding international frameworks to guide transboundary cooperation, enhancing cooperation among non-state actors, and ensuring the representativeness and fairness of non-Arctic actors' participation. This research provides insights into transboundary environmental management under political tensions, while also offering new pathways for analysing large-scale environmental governance structures.

Political relations and trade cooperation of the Belt and Road initiative: research on trade network based on strategic emerging industries

PurposeThe purpose of this study is to examine the impact of political relations on trade in strategic emerging industries (SEIs) in the Belt and Road initiative (BRI) associated countries. This investigation encompasses not only from the perspective of bilateral political relations but also the political intervention of third parties.Design/methodology/approachThe study employs the temporal exponential random graphmodel to analyze the dynamic structure and influencing factor of SEIs trade network among 150 BRI-associated countries from 2015 to 2020.FindingsThe results indicate that the trade of SEIs in the BRI-associated countries exhibits a pattern of concentrated exporters and decentralized importers. Amicable bilateral political relations foster trade cooperations in SEIs, while political pressure from the United States has the opposite effect. Furthermore, compared with the influence of third parties, the BRI has created a more robust trade environment characterized by political mutual trust.Practical implicationsBRI-associated countries should strengthen their political communication, and endeavor to transform political consensus and shared vision into concrete collaborative projects, while mitigating geopolitical uncertainties through a sound risk evaluation system. Moreover, they should establish a more transparent and consistent consultation mechanism and leverage the BRI trade network to foster balanced and mutually beneficial partnerships that minimize rivalry and dependence on a single market.Originality/valueThis study goes beyond observed trade cost and incorporates the political factor into the determinants of the BRI trade, thereby expanding the theoretical boundaries of existing BRI research. Also, this study employs bilateral trade data to construct SEIs trade networks (SEITNs) along the BRI route. It provides a comprehensive understanding of the dynamic determinates of the SEITNs will provide valuable practical guidance for enhancing and expanding trade and cooperation among BRI-associated countries.

Analysis of the Spatial Correlation Network and Driving Mechanism of China’s Transportation Carbon Emission Intensity

From 2008 to 2021, this study analyzed the spatial correlation characteristics between provincial transportation carbon emission intensity and explored ways to reduce transportation carbon emissions. This study used the modified gravity model, social network analysis (SNA) method, and temporal exponential random graph model (TERGM) to analyze the spatial correlation network evolution characteristics and driving mechanism of China’s transportation carbon emission intensity. This study found that China’s transportation carbon emission intensity and spatial correlation network have unbalanced characteristics. The spatial correlation network of transportation carbon emission intensity revealed that Shanghai, Beijing, Tianjin, Guangdong, Fujian, and other provinces were at the center of the network, with significant intermediary effects. The spatial correlation of transportation carbon emission intensity was divided into four functional plates: “two-way spillover”, “net benefit”, “broker”, and “net spillover”. The “net benefit” plate was mainly located in developed regions, and the “net spillover” plate was primarily located in underdeveloped regions. Endogenous structural and exogenous mechanism variables were the main factors affecting the evolution of the spatial correlation network of provincial transportation carbon emission intensity.

Social network analysis to predict social behavior in dairy cattle

Dairy cattle are frequently housed in free stalls with limited space, impacting social interactions between individuals. Social behavior in dairy cattle is gaining recognition as a valuable tool for identifying sick animals, but its application is hampered by the complexities of analyzing social interactions in intensive housing systems. In this context, precision livestock technologies present the opportunity to continuously monitor dyadic spatial associations on dairy farms. The aim of this study is to evaluate the accuracy of predicting social behavior of dairy cows using social network analysis. Daily social networks were built using the position data from 149 cows over 14 consecutive days of the study period. We applied the separable temporal exponential random graph models to estimate the likelihood of formation and persistence of social contacts between dairy cows individually and to predict the social network on the subsequent day. The correlation between the individual degree centrality values, the number of established social contacts per individual, between the predicted and observed networks ranged from 0.22 to 0.49 when the structural information from network triangles was included in the model. This study presents a novel approach for predicting animal social behavior in intensive housing systems using spatial association information obtained from a real-time location system. The results indicate the potential of this approach as a crucial step toward the larger goal of identifying disruptions in dairy cows expected social behavior.

Life-Space Mobility, Transportation, and the Companionship Network of Members of a Hispanic Senior Center.

Companions (i.e., friends who spend time together) are important for the well-being of older adults. Senior centers in the United States are places for older adults to participate in group activities and form and maintain companionships. However, differences in mobility and transportation may affect the ability of older adults to leverage senior center activities into actual companionships. This social network analysis was conducted to characterize the companionship network among members of a senior center in relation to their life-space mobility and transportation resources. An exponential random graph model was estimated to identify mobility- and transportation-related correlates of the likelihood of a companionship tie among senior center members (N = 42). Members had an average of 2 companionships with one another (M = 2.2, SD = 2.7). Companionships were more likely for members with greater life-space mobility (p = .009), who attended the senior center more frequently (p = .004), with automobile ownership in their households (p = .034), and who were not transportation cost-burdened (i.e., spent less than 15% of their income on transportation, p = .005). Demographic characteristics, limitations on instrumental activities of daily living, and being at risk for depression were not significantly associated with the likelihood of companionships. These findings extend previous knowledge of the role of life-space mobility and transportation in supporting general social participation for older adults to include the importance of transportation and mobility for having companions within a senior center.

Organised crime movement across local communities: A network approach

AbstractThis paper explores the structure of organised crime movement across local communities and the drivers underpinning such movement. Firstly, it builds on network analysis to offer a novel methodological approach to empirically and quantitatively study the movement of organised crime offenders across geographical areas. The paper then applies this approach to evidence from Cambridgeshire in the United Kingdom. It reconstructs the movement of organised crime members across local areas based on a large-scale police dataset that includes 41 months of recorded crime events. It identifies organised crime “turf” and “target” areas and then explores the drivers of movement from the former to the latter using Exponential Random Graph Models. Findings confirm that geographical distance matters; however, socio-demographic, urban, economic and crime-related characteristics of communities play a key role. Organised crime group members target urban communities with higher than average illegal market opportunities (proxied by drug-related activity). The work also finds the effect of socio-demographic homophily between turf and target communities, suggesting that organised crime group members might target territories that are similar to their own. While a high level of deprivation makes a community more likely to send organised crime members, its impact on a community’s probability of being a receiver is less clear. Finally, the paper offers a way to identify communities (local areas) at risk of being targeted by criminal organisations, thus providing practitioners with a tool for early interventions.

Connectivity at the human-wildlife interface: starling movements relate to carriage of E. coli

Synanthropic bird species in human, poultry or livestock environments can increase the spread of pathogens and antibiotic-resistant bacteria between wild and domestic animals. We present the first telemetry-based spatial networks for a small songbird. We quantified landscape connectivity exerted by spotless starling movements, and aimed to determine if connectivity patterns were related to carriage of potential pathogens. We captured 28 starlings on a partridge farm in 2020 and tested them for Avian influenza virus, West Nile virus WNV, Avian orthoavulavirus 1, Coronavirus, Salmonella spp. and Escherichia coli. We did not detect any viruses or Salmonella, but one individual had antibodies against WNV or cross-reacting Flaviviruses. We found E. coli in 61 % (17 of 28) of starlings, 76 % (13 of 17) of which were resistant to gentamicin, 12 % (2 of 17) to cefotaxime/enrofloxacin and 6 % (1 of 17) were phenotypic extended spectrum beta-lactamase (ESBL) carriers. We GPS-tracked 17 starlings and constructed spatial networks showing how their movements (i.e. links) connect different farms with nearby urban and natural habitats (i.e. nodes with different attributes). Using E. coli carriage as a proxy for acquisition/dispersal of bacteria, we found differences across spatial networks constructed for E. coli positive (n = 7) and E. coli negative (n = 9) starlings. We used Exponential Random Graph Models to reveal significant differences between networks. In particular, an urban roost was more connected to other sites by movements of E. coli positive than by movements of E. coli negative starlings. Furthermore, an open pine forest used mainly for roosting was more connected to other sites by movements of E. coli negative than by movements of E. coli positive starlings. Using E. coli as a proxy for a potential pathogen carried by starlings, we reveal the pathways of spread that starlings could provide between farms, urban and natural habitats.

Does Formal Interorganizational Relationship Shape Interpersonal Knowledge Exchange in Multiteam Construction Projects? A Multi-Level Network Analysis

This study investigates the differences in structural patterns of intra-and inter-team knowledge exchange networks in multiteam construction projects and explores the cross-levelinteraction between knowledge exchange networks and interorganizational formal networks, as represented by networks of contractual and task interdependence relationships. An empirical study is developed by using multiple exponential random graph models. The findings show that a discrepancy between intra- and inter-team knowledge exchange structural patterns arises with respect to reciprocity. Interorganizational task interdependencies are found to align with cross-team knowledge exchange. Significant homophily in work experience is found in cross-team knowledge exchange. Interorganizational task interdependence promotes cross-team knowledge exchange among project members with heterogeneous expertise. This study expands the research on project-based multiteam system knowledge exchange networks from a multi-level network analysis perspective.

Participatory approaches and Social Network Analysis to analyse the emergence of collective action for rural development: a case study in the Spanish Pyrenees

This paper explores the social capital of an emergent beef quality brand in two valleys (Ansó and Hecho) in the Spanish Pyrenees through a combination of participatory approaches (focus groups) and Social Network Analysis. We organised three focus groups with stakeholders from the administration, tourism and commerce, and farming sectors to evaluate the interest for the initiative. In the Social Network Analysis, we surveyed 32 local stakeholders to construct an advisory network, including the surveyed actors and non-surveyed actors that they mentioned, and a trust network, including only the surveyed actors and their trust level in each other. The trust network was analysed using the Louvain’s algorithm to identify potential internal communities and the exponential random graph model (ERGM) to infer the shaping effects of actors’ attributes on the network’s structure. Our results revealed a general interest of the stakeholders in the beef quality brand and a sparse network characterised by a propensity to mutual and open interactions with four clusters based on employment sectors/educational levels and connected by two prominent actors. Therefore, the social capital of the initiative showed both risks because a loss of these few prominent actors could inhibit the network, and opportunities, because their presence, along with the mutual and open interactions, would ensure efficient information exchange. The investigation revealed also two possible limiting factors: the absence of a coordinator and the actors’ unwillingness to assume this role. The results confirmed the efficacy of the methodology used to investigate the social capital of emergent initiatives, but further research should be carried out including stakeholders’ concerns on its economic implications. HIGHLIGHTS Focus groups and Social Network Analysis allowed to explore the social capital of a beef production quality brand initiative. A sparse network with mutual interactions but few central actors emerged. The absence of a coordinator and unwillingness to cover the role are possible limiting factors.

The structural change and determinants of global carbon footprint network embodied in international migration: A social network analysis

The carbon footprint embodied in international population mobility has a complex spatial correlation, which exacerbates the difficulty of global climate governance. However, the features, changing trends, and determinants of the carbon footprint flow embodied in global population migration are poorly understood. This study employed social network analysis and the exponential random graph model (ERGM) to investigate the structural changes and drivers of the carbon footprint network embodied in global migration from 1995 to 2015. The results showed that approximately 50% of carbon footprint flow embodied in international migration flowed from developing to developed countries. The spatial connections between countries in the network were becoming increasingly close, displaying a typical small-world structure, and showing low reciprocity and negative assortativity. Moreover, centrality analysis highlighted the United States and the European Union as being at the core of the network, whereas some emerging economies (e.g., China, India, and South Africa) were shown as having an increasing influence on the network. The determinants of network formation were divided into three effects. For node attribute effect, countries with developed economy, high proportion of industrial value added, urbanization and openness were becoming the destinations of carbon inflows from immigrants, while countries with high consumption of renewable energy and energy intensity had a trend of carbon outflows with emigrants over periods. As for exogenous network effect, the significance of economic integration on the formation of the network was strengthening, while that of geographical proximity and cultural similarity was declining. Additionally, the positive impact of self-organizational effect on the network was decreasing. This study provided guidance for countries to formulate policies to reduce the carbon emissions embodied in international migration.

What factors contribute to the formation of interorganizational collaboration networks: A longitudinal study in response to COVID‐19 emergency

AbstractIn the realm of addressing large‐scale emergencies, interorganizational collaboration emerges as a crucial strategy for effectively mitigating crises. However, the existence of institutional and procedural disparities poses significant challenges in establishing collaborative networks among diverse organizations. Moreover, the determinants influencing cooperative formations exhibit heterogeneity across distinct network structures. This research employs proximity theory and the Exponential Random Graph Model (ERGM) to comprehensively examine the dynamic evolution of interorganizational collaboration networks. Our primary objective is to investigate the effects of institutional proximity, organizational proximity, geographical proximity and social proximity on the formation of interorganizational collaboration networks. Textual data was meticulously collected from the Wisers database and official websites. The findings underscore the pivotal roles of institutional and organizational proximity in fostering interorganizational collaboration networks, while the impact of geographical proximity is relatively insignificant. Additionally, we delve into the driving factors that influence the formation of diverse structural networks. Social proximity emerges as a significant catalyst for horizontal collaborative networks but impedes the establishment of vertical collaborative relationships. Furthermore, we partition the entire emergency response process into six stages to explore the dynamic evolution characteristics of these networks. The results reveal an overall decentralized structure with a tendency toward clustering in the collaborative network. These findings hold substantial significance as they elucidate the influential factors driving the formation of diverse collaborative networks in a dynamic context. Therefore, this research bears implications for policymakers and practitioners aiming to enhance their emergency response capabilities with optimal efficiency.

Investigating the Coverage of China’s Vaccine Crisis on YouTube: Networked Framing, Grassroots Activism, and Homophily

ABSTRACT This study investigates how China’s domestic vaccine crisis in 2018 was politicized within Chinese video content on YouTube. The analysis, employing the Structural Topic Model, uncovered a notable hyper-politicization of the vaccine crisis in video comments, often diverging from the issue frames presented in video titles. Furthermore, inferential network analysis using the Exponential Random Graph Model revealed the hyper-politicized comments can be attributed to 1) the emergence of networked commenters engaging in cross-video commenting practices, and 2) political homophily among video producers, particularly regarding their critical political stance toward China. The findings suggest the networked dynamics of ssue politicization.

자동차 산업 공급 네트워크의 상류와 하류 구성 동인의 비교

Modern industry is a competition between supply networks. Knowing and managing the supply network well is an essential survival requirement for a company. There is a difference in orientation between the upstream and downstream of the supply network, which causes structural differences between the upstream and downstream. This study aims to reveal the differences in the configuration drivers of the upstream and downstream automotive supply networks. Using transaction data from the automotive industry, we compared the drivers that constitute supply networks and create upstream and downstream links. The Exponential Random Graph Models (ERGMs) were used to evaluate the significance of configuration drivers, including company attributes, dyadic attributes, and structural characteristics. As a result, the age of supplier, size of the purchaser, sales of the purchaser, asset of the purchaser, transitivity, and activity were found to be common drivers of upstream and downstream. On the other hand, the age of the purchaser, the difference in sales between the two companies, and popularity were found to be significant drivers only in the downstream network. These research results are the first to empirically compare the upstream and downstream supply networks, and have academic contribution as the first step in finding the cause of the differences in network structure between the upstream and downstream networks.

Evolution of structural properties of the global strategic emerging industries' trade network and its determinants: An TERGM analysis

This article examines the evolution of the global strategic emerging industries trade networks (GSEITNs) and employs the temporal exponential random graph model (TERGM) to investigate the principal determinants of the critical trade cooperation in the global strategic emerging industries (SEIs) from 2007 to 2020. The findings indicate that GSEITNs exhibit a sparse, hierarchical, and monopolistic structure. Despite the continual integration of emerging nations into GSEITNs, countries have grown progressively wary of the network. Moreover, the structure properties of the endogenous network proves that SEIs trade links have a substantial signaling effect. More importantly, SEIs trade is more reliant on the economic size and innovation capacity, and countries are more cautious about protecting their intellectual property, especially when trading with countries possessing similar R&D levels. Furthermore, robust bilateral political ties can augment the probability of SEIs trade between two countries, particularly in sectors such as new energy vehicles and new-generation information technology industries. In summary, this article takes into account the influence of network structure, national attributes and external networks, and provides new insights into studying trade network evolution in SEIs.

What shapes the formation of interstate benchmarking networks?

AbstractMost studies on public sector benchmarking focus on performance indicators, processes, and outcomes of managed benchmarking. This article, instead, explores the formation of spontaneous interstate benchmarking networks among U.S. state agency leaders. Informed by social comparison theory, we first recategorize benchmarking into best practice benchmarking and competitive benchmarking. Then, we quantify two benchmarking networks with a survey dataset and employ the Exponential Random Graph Model to analyze both endogenous and exogenous factors in the formation of both types of benchmarking networks. We find that the best practice benchmarking network has a popularity effect, while the competitive benchmarking network has mutuality and transitivity effects. Both types of benchmarking networks are more likely to form among states with historical policy diffusion ties and similar economic and geographic characteristics. This study contributes to the literature on public sector benchmarking and network research by exploring the factors that influence the formation of benchmarking networks.