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Technology cooperation and technology transfer can be beneficial for realising the flow of innovative resources and promoting technological progress and growth, which is crucial to achieving sustainable and coordinated economic development. We investigate the structural characteristics of interprovincial innovation multilayer network, also called technology cooperation and transfer network, including the technology cooperation sub-network and technology transfer sub-network. Data are derived from Patent Information Service Platform for 2008–2018. The results are as follows: (1) the two sub-networks and multilayer network show the increasing density, and all of those networks display large cluster coefficients and short average paths. The degree between different networks is highly relevant. GDP, indegree and outdegree are linearly related. (2) As the two sub-networks in different provinces become increasingly integrated, the core provinces control the flow in the network and the edge provinces surround the core provinces, indicating a clear 'core-edge' structure. (3) The exponential random graph model regression results demonstrate a significant 'path dependence' property for technology cooperation-transfer network. GDP, R&D, distance and railway freight flow have a significant impact on network format. [ABSTRACT FROM AUTHOR]

The collection of data on populations of networks is becoming increasingly common, where each data point can be seen as a realisation of a network-valued random variable. Moreover, each data point may be accompanied by some additional covariate information and one may be interested in assessing the effect of these covariates on network structure within the population. A canonical example is that of brain networks: a typical neuroimaging study collects one or more brain scans across multiple individuals, each of which can be modelled as a network with nodes corresponding to distinct brain regions and edges corresponding to structural or functional connections between these regions. Most statistical network models, however, were originally proposed to describe a single underlying relational structure, although recent years have seen a drive to extend these models to populations of networks. Here, we describe a model for when the outcome of interest is a network-valued random variable whose distribution is given by an exponential random graph model. To perform inference, we implement an exchange-within-Gibbs MCMC algorithm that generates samples from the doubly-intractable posterior. To illustrate this approach, we use it to assess population-level variations in networks derived from fMRI scans, enabling the inference of age- and intelligence-related differences in the topological structure of the brain’s functional connectivity. [ABSTRACT FROM AUTHOR]

• The Advocacy Coalition Framework focuses on beliefs external to policy networks. • Micro-structural network processes are also key drivers of informal policy networks. • We combine these approaches in an ERGM of a Canadian collaboration policy network. • Beliefs about carbon taxes, LNG and oil sands are related to tie formation. • Reciprocity, local connectivity and transitive closure are also related to tie formation. Extant research on policy networks tends to focus on explaining successes and/or failures of particular policy efforts. One commonly used theoretical framework – the Advocacy Coalition Framework (ACF) – focuses on actor attributes external to policy networks. We argue this leads to an incomplete understanding of the social dynamics of climate change policy making. We incorporate a policy network analytic approach with the ACF in an ERGM of collaboration in a Canadian climate change policy network, showing the role micro-structural network processes play in giving rise to informal policy networks. We find certain policy beliefs are correlated with tie formation. We also find micro-structural network processes related to reciprocity, structural equivalence and transitive closure are correlated with tie formation. We argue combining these two prominent streams of policy network literature has potential to improve our understanding of climate change policy making processes. [ABSTRACT FROM AUTHOR]

The European Commission has central authority over the European Union's merger control. It is therefore responsible for making important merger decisions that are bound to affect vast markets and EU and non-EU market players, with significant implications for wider economic, political, and societal segments. These decisions are often informed by theoretical and ideational insights from various schools of competition analysis and economic theories, or they are guided by non-economic objectives. This article analyzes merger cases cleared under the current European Union Merger Regulation (EUMR) in an attempt to reveal whether the following insights underpin the Commission's merger control: the Chicago School, Harvard School, Freiburg School, and considerations for Single Market integration. The analysis is conducted by employing an exponential random graph model (ERGM) approach. [ABSTRACT FROM AUTHOR]

Global trade of renewable energy products has increased significantly in recent years. This paper constructs an analytical framework of a global trade network for renewable energy products based on bilateral trade data between 2009 and 2019. It analyses its structural evolution at the global and local levels and investigates the influencing factors of the network with the Exponential Random Graph Model. The empirical results indicate that countries in the trade network have become more closely connected, featuring a core-periphery and increasing reciprocity relationship. China, Germany, and Japan have remained in the position of core countries; China has especially been prominent among core countries. Our empirical results verify that the sender-receiver effects can explain the evolution of this global trade network. The empirical results also indicate that the climate change agreement network and the common border network have positive effects on the formation of the trade network. As regards political implications, the core countries in the trade network should optimize the layout of renewable energy development and improve infrastructure accordingly. Countries should also jointly build a more fair and reasonable multilateral system that fulfills their responsibilities. [ABSTRACT FROM AUTHOR]

From the network perspective, this paper explores the effect of FDI on trade and analyzes the structural effect on different sectors classified by technology levels among the Belt and Road (B&R) countries. Employing social network analysis methods, we depict the evolution of FDI networks and trade networks of B&R countries and find that both two networks are disassortative and small-world networks as well as the density and number of edges increase. Through the results of ERGM, we get three findings about the effect of FDI on trade. (1) FDI has the positive effect on trade among B&R countries from the network perspective; (2) the positive effect of FDI on trade has improved since the Belt and Road Initiative is proposed; (3) FDI promotes the exports of low technological sector, medium technological sector, and high technological sector, but the effect on medium technological sector is stronger than the other two sectors. [ABSTRACT FROM AUTHOR]

Housing resilience planning is a challenging process that requires active participation of multisector stakeholders, including public agencies, private industries, nongovernmental organizations (NGOs), academia, and community residents. Despite the importance of multisector stakeholder collaboration and engagement, there is limited understanding of how stakeholders collaborate and engage in housing resilience planning. To address this gap, this study used social network analysis (SNA) to analyze how multisector stakeholders collaborate in producing housing resilience-focused plans, reports, and guidelines. A bipartite SNA model was built based on the data collected from 39 housing resilience planning documents in the Greater Miami and the Beaches (GM&B) region, including the City of Miami, the City of Miami Beach, and Miami-Dade County. Three network centrality measures were computed, and exponential random graph model (ERGM) analysis was performed to analyze the network. This study found that there are significant differences in the centrality measures across different stakeholder sectors. Among them, public agencies and academic stakeholders contributed more to housing resilience planning, whereas the involvement of community residents was relatively limited compared with that of the other sectors. In addition, the results suggest that more-balanced, decentralized, and intersector collaboration mechanisms may be needed to enhance housing resilience planning. The findings from this study offer knowledge of and insight into how to facilitate more-effective multisector stakeholder collaboration in planning for housing resilience. [ABSTRACT FROM AUTHOR]

This article studies the formation of a local Creative Placemaking (CPM) policy network based on the Advocacy Coalition Framework (ACF) and social capital perspective of policy networks. This article hypothesizes that policy beliefs, policy learning, social capital, and the perceived risks induced by defections, as well as macro‐level changes in the broader political and socio‐economic system, influence partner selection in the local CPM policy networks. The study collected survey data from policy actors participating in the Franklinton CPM‐catalyzed revitalization project in Columbus, Ohio. An exponential random graph model (ERGM) was applied to test the hypotheses. The findings partially support the hypotheses: policy learning, certain secondary policy beliefs, and risk perceptions of changes in economic and political factors are correlated with tie formation. The reciprocity‐driven bonding structure underlies the Franklinton CPM policy network, suggesting that policy actors perceive the Franklinton CPM policy network as a high‐defection‐risk network. This study has implications for policy makers in designing engagement strategies to better involve stakeholders holding different beliefs and occupying different network positions. [ABSTRACT FROM AUTHOR]

Background: Building a reputable network on Twitter is viewed as impactful in several scholarly disciplines, but little is known about the professional and interdisciplinary human-computer interaction (HCI) community. This study combined two approaches from scholarly communication and technical communication to capture the static and dynamic features of the HCI scholar Twitter network. Literature review: Related studies that described the scholarly reputation built through Twitter and social networking in the field of HCI were reviewed and discussed. Research questions: 1. In which countries are HCI scholars more likely to follow their peers in the same country? 2. What are the characteristics (country, reputation) and actions (reciprocity) of HCI scholars who are more likely to build HCI scholarly network profiles on Twitter? Research methodology: The network analysis method of the exponential random graph model (ERGM) was adopted to trace and visualize current follower networks on Twitter. Results and discussion: We found that 22.9% of HCI scholars use Twitter and that reciprocity and country of current employment best drive the Twitter connections of scholars. Characteristics of HCI scholars’ tie formation online are also illustrated and discussed. Implications for practice: This study contributes to field studies of professional networks by identifying the structural properties and factors that influence scholars’ search for professional development on Twitter. The empirical findings should be a helpful reference for HCI professional societies and individual scholars in operating online professional networks. [ABSTRACT FROM AUTHOR]

Environmental governance is complex because it addresses challenges anchored in different sectors and concerns multiple interdependent issues. Managing those complex interdependencies through collaboration is vital for efficient long-term environmental governance. However, because interdependencies between environmental issues are challenging to unravel and vastly complex, it is challenging for actors to account for them when deciding with whom to collaborate. I use the concept of social-ecological networks to study interdependencies among actors and environmental issues and ask how the quality of actor-issue interdependencies influences collaboration patterns. Based on the actor-issue network, I account for interdependencies based on three distinct qualities of actorissue paths, i.e., (i) length of actor-issue paths: how closely actors are connected by environmental issues, (ii) multiplexity of actor-issue paths: if actors have multiple parallel paths connecting them through environmental issues, and (iii) similarity of actor-issue paths: whether actors' environmental impact is similar to one of their potential collaboration partners. Using exponential random graph models and data on eight Swiss wetlands, a qualitative meta-regression analysis of the results reveals that the three qualities of actorissue interdependencies influence collaboration patterns between actors. Whether the impact of actor-issue interdependencies on the probability of collaboration ties is positive or negative largely depends on the complexity of the governance situations. Only in situations with homogeneous case areas and under the absence of borders (low network exogenous governance complexity) as well as in the presence of many actors do the length, multiplexity, and similarity of actor-issue interdependencies have a clear, positive impact on the formation of collaboration ties. Although the comparative setting helps identify specific governance settings where the hypotheses are supported, it also reveals the importance of multi-case studies to compare contextual differences between cases. [ABSTRACT FROM AUTHOR]

Understanding the regularity and determinants of mobility is indispensable for the reasonable deployment of shared bicycles and urban planning. A spatial interaction network covering streets in Beijing's six main districts, using bike sharing data, is constructed and analyzed. as Additionally, the exponential random graph model (ERGM) is used to interpret the influencing factors of the network structure and the mobility regularity. The characteristics of the spatial interaction network structure and temporal characteristics between weekdays and weekends show the following: the network structure on weekdays is obvious; the flow edge is always between adjacent blocks; the traffic flow frequently changes and clusters; the network structure on weekends is more complex, showing scattering and seldom changing; and there is a stronger interaction between blocks. Additionally, the predicted result of the ERGM shows that the influencing factors selected in this paper are positively correlated with the spatial interaction network. Among them, the three most important determinants are building density, housing prices and the number of residential areas. Additionally, the determinant of financial services shows greater effects on weekdays than weekends. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has fundamentally reshaped global socio-economic structures, precipitating a profound transformation in people's lifestyles. An in-depth analysis of the disruptions in post-pandemic population returns patterns and the evolving driving factors can facilitate socio-economic recovery, development, and macro-control. This paper employs social network analysis to examine the spatial patterns and evolving urban locational attributes of the Population Mobility Network (PMN) during post-holiday returns in major Chinese cities amid the pandemic, assessing the impact of varying proximities on the PMN. The results indicate that: (1) During the pandemic, the geographic patterns of population mobility in China underwent significant fluctuations, with distinct regional and temporal variations, while intercity connections increasingly shifted toward shorter trips. (2) From 2019 to 2022, major Chinese cities experienced notable changes in status and connectivity, evolving into a multi-centric structure within the national population mobility network. (3) Community clusters predominantly adhere to provincial demarcations, maintaining stable structures within major urban agglomerations despite intense intra-regional competition, with clear distinctions in community stability between northern and southern regions. (4) Beyond traditional socio-economic factors, the level of digital finance has emerged as a new driver of population mobility, with the varied attributes of inter-city relationships also significantly influencing the PMN. Studying spatial patterns and urban location from the perspective of population mobility can inform post-pandemic optimization of national resource allocation and urban recovery strategies. • Employed social network analysis to explore post-pandemic population mobility patterns in Chinese cities. • Identified significant fluctuations in mobility intensity, with temporal and regional variations. • Revealed the emergence of a polycentric mobility structure, led by newly prominent cities. • Found employment factors and online services levels significantly influence mobility networks. • Highlighted the importance of understanding spatial patterns and urban locations for post-pandemic recovery strategies. [ABSTRACT FROM AUTHOR]

Many contemporary social and ecological challenges in forested ecosystems (climate change, invasive species, wildland-urban interface development, and wildfires) span multiple jurisdictions and are characterized by complex patterns of social and ecological interdependencies. Increasing evidence suggests that interdependent risk can best be addressed by working across boundaries (jurisdictional, scalar, and expertise) by sharing information and cooperating in management activities. Polycentric governance has emerged as a framework to understand how multiple and overlapping centers of decision-making authority establish and maintain governance connectivity to solve collective action problems and interdependent risks. Previous studies have examined the collaborative and interorganizational process of polycentric landscape governance, yet most studies rely on qualitative case study data or descriptively employ social network analysis. Understanding the values, beliefs, and motivations of actors (land managers, landowners, researchers, policymakers, and non-governmental organizations) for cooperating is important for improving polycentric governance design, implementation, and operation. How the context and characteristics of social-ecological systems shape polycentric governance remains largely unexplored. On the basis of research in the Sequoia-Kings Canyon Protected Area-Centered Ecosystem, we address this gap by utilizing exponential random graph modeling to analyze the social and ecological drivers of polycentric wildfire governance. This research highlights that even in situations of high stakes (increasing occurrences of high-severity wildfires that escape suppression) actors will collaborate only if the gains from collaboration outweigh the costs. If jurisdictions or other organizations are thought to have low operational capacity or lack useful information, even with a high probability of large wildfire, the actor-to-actor connections are less likely for effective polycentric governance. Our results highlight previously undiscussed mechanisms of network formation in wildfire hazard governance, and we discuss the broader applicability for forest landscape challenges and for polycentric governance design and assessment in other social-ecological contexts.

Graduate students face a variety of stressors that relate to poor academic performance, lower completion rates, and high rates of depressive symptoms, anxiety, and stress. Student peers provide supports for coping with these stressors and succeeding academically. However, graduate students may find it difficult to form relationships with their peers. This mixed method social network analysis (MMSNA) examines graduate student perceptions of their peer relationships and the factors associated with peer friendships in a program that placed students in cohorts for the first semester of a two-year Master of Social Work program. Findings from three student focus groups include four main themes related to the cohort system and the tendency for students to gravitate to others who were similar to them (i.e., homophily). Focus group findings informed model specification for a curved exponential family model of student friendships at the end of the third semester of the program (N = 70) that identified correlates of student friendships including statistically significant direct and homophily effects for age, gender, and race/ethnicity. First-semester relationships and shared classes in subsequent semesters also increased the likelihood of friendships in the third semester. Findings emphasize the value of cohorts and a diverse student body for peer relationships. [ABSTRACT FROM AUTHOR]

As an interdisciplinary field, data-driven journalism integrates the intellectual origins of investigative journalism, computer-assisted reporting, and the emerging paradigm of computational social science. Studies of news production have revealed, however, that news professionals are reinforcing existing power structures via an interpretive community, where homophily-evoked social interactions—even in the social media context—create echo chambers and discussion fragmentation. Is the representation of data-driven journalism in the electronic public sphere breaking boundaries among people from different domains or does it resemble the existing power structure? This study adopts a network analytics approach and constructs a representational network among actors who joined the public discussion of data-driven journalism in the Twittersphere—the co-retweeted network—such that two accounts are connected if their tweets are retweeted by the same user. Public tweets containing search queries related to data-driven journalism published from February 2017 to February 2018 were collected with Twitter real-time streaming application programming interface (API). A co-retweeted network with 1,148 accounts was derived from verified accounts' retweeting posts. Results found that several communities emerged, and news organizations, nongovernmental and nonprofit professional organizations, and academic institutions were in the crucial positions of the network. The exponential random graph models (ERGMs) based on this network revealed the extent to which gender, geographical location, and institutional type of the users were associated with the tie-formation. This study documents the major actors who are discussing the subject of data-driven journalism and raises critical reflections toward the interdisciplinary collaboration in the production of public knowledge. [ABSTRACT FROM AUTHOR]

Environmental governance is complex because it addresses challenges anchored in different sectors and concerns multiple interdependent issues. Managing those complex interdependencies through collaboration is vital for efficient long-term environmental governance. However, because interdependencies between environmental issues are challenging to unravel and vastly complex, it is challenging for actors to account for them when deciding with whom to collaborate. I use the concept of social-ecological networks to study interdependencies among actors and environmental issues and ask how the quality of actor-issue interdependencies influences collaboration patterns. Based on the actor-issue network, I account for interdependencies based on three distinct qualities of actor-issue paths, i.e., (i) length of actor-issue paths: how closely actors are connected by environmental issues, (ii) multiplexity of actor-issue paths: if actors have multiple parallel paths connecting them through environmental issues, and (iii) similarity of actor-issue paths: whether actors’ environmental impact is similar to one of their potential collaboration partners. Using exponential random graph models and data on eight Swiss wetlands, a qualitative meta-regression analysis of the results reveals that the three qualities of actor-issue interdependencies influence collaboration patterns between actors. Whether the impact of actor-issue interdependencies on the probability of collaboration ties is positive or negative largely depends on the complexity of the governance situations. Only in situations with homogeneous case areas and under the absence of borders (low network exogenous governance complexity) as well as in the presence of many actors do the length, multiplexity, and similarity of actor-issue interdependencies have a clear, positive impact on the formation of collaboration ties. Although the comparative setting helps identify specific governance settings where the hypotheses are supported, it also reveals the importance of multi-case studies to compare contextual differences between cases.

Understanding the regularity and determinants of mobility is indispensable for the reasonable deployment of shared bicycles and urban planning. A spatial interaction network covering streets in Beijing’s six main districts, using bike sharing data, is constructed and analyzed. as Additionally, the exponential random graph model (ERGM) is used to interpret the influencing factors of the network structure and the mobility regularity. The characteristics of the spatial interaction network structure and temporal characteristics between weekdays and weekends show the following: the network structure on weekdays is obvious; the flow edge is always between adjacent blocks; the traffic flow frequently changes and clusters; the network structure on weekends is more complex, showing scattering and seldom changing; and there is a stronger interaction between blocks. Additionally, the predicted result of the ERGM shows that the influencing factors selected in this paper are positively correlated with the spatial interaction network. Among them, the three most important determinants are building density, housing prices and the number of residential areas. Additionally, the determinant of financial services shows greater effects on weekdays than weekends.

Gang studies often use location-based approaches to explain gang members' interconnectedness. Although this perspective remains consistent with the proximity principle that the smaller the geographic space, the greater the likelihood of observing connections between individuals, location-based studies limit our understanding of gang member connections to narrowly defined geographic spaces at specific points in time. The advent of social media has re-spatialized gang member interconnectedness to unbounded geographic spaces, where the preservation of online activity can extend indefinitely. Despite having an online presence, most research examining the digital footprint of gangs tends to be descriptive. This study collects Twitter data to analyze the geospatial distribution of gang member connections using an exponential random graph model (ERGM) of location homophily. An ERGM analyzes network substructures to determine the patterns of relationships between vertices. In this case, the extent to which homophily by city, state, and gang affiliation determine gang member connections. The results of this study support the proximity principle but challenge the assertion that gangs are strictly localized. [ABSTRACT FROM AUTHOR]

The brain can be modelled as a network with nodes and edges derived from a range of imaging modalities: the nodes correspond to spatially distinct regions and the edges to the interactions between them. Whole-brain connectivity studies typically seek to determine how network properties change with a given categorical phenotype such as age-group, disease condition or mental state. To do so reliably, it is necessary to determine the features of the connectivity structure that are common across a group of brain scans. Given the complex interdependencies inherent in network data, this is not a straightforward task. Some studies construct a group-representative network (GRN), ignoring individual differences, while other studies analyse networks for each individual independently, ignoring information that is shared across individuals. We propose a Bayesian framework based on exponential random graph models (ERGM) extended to multiple networks to characterise the distribution of an entire population of networks. Using resting-state fMRI data from the Cam-CAN project, a study on healthy ageing, we demonstrate how our method can be used to characterise and compare the brain’s functional connectivity structure across a group of young individuals and a group of old individuals.

Graduate students face a variety of stressors that relate to poor academic performance, lower completion rates, and high rates of depressive symptoms, anxiety, and stress. Student peers provide supports for coping with these stressors and succeeding academically. However, graduate students may find it difficult to form relationships with their peers. This mixed method social network analysis (MMSNA) examines graduate student perceptions of their peer relationships and the factors associated with peer friendships in a program that placed students in cohorts for the first semester of a two-year Master of Social Work program. Findings from three student focus groups include four main themes related to the cohort system and the tendency for students to gravitate to others who were similar to them (i.e., homophily). Focus group findings informed model specification for a curved exponential family model of student friendships at the end of the third semester of the program (N = 70) that identified correlates of student friendships including statistically significant direct and homophily effects for age, gender, and race/ethnicity. First-semester relationships and shared classes in subsequent semesters also increased the likelihood of friendships in the third semester. Findings emphasize the value of cohorts and a diverse student body for peer relationships.

Examining coauthorship networks is key to study scientific collaboration patterns and structural characteristics of scientific communities. Here, we studied coauthorship networks of sociologists in Italy, using temporal and multi-level quantitative analysis. By looking at publications indexed in Scopus, we detected research communities among Italian sociologists. We found that Italian sociologists are fractured in many disconnected groups. The giant connected component could be split into five main groups with a mix of three main disciplinary topics: sociology of culture and communication (present in two groups), economic sociology (present in three groups) and general sociology (present in three groups). By applying an exponential random graph model, we found that collaboration ties are mainly driven by the research interests of these groups. Other factors, such as preferential attachment, gender and affiliation homophily are also important, but the effect of gender fades away once other factors are controlled for. Our research shows the advantages of multi-level and temporal network analysis in revealing the complexity of scientific collaboration patterns. [ABSTRACT FROM AUTHOR]

Exponential family random graph models (ERGM) are increasingly used in the study of social networks. These models are build to explain the global structure of a network while allowing inference on tie prediction on a micro level. The number of papers within economics is however limited. Possible applications for economics are however abundant. The aim of this document is to provide an explanation of the basic mechanics behind the models and provide a sample code (using R and the packages statnet and ERGM) to operationalize and interpret results and analyse goodness of fit. After reading this paper the reader should be able to start their own analysis. [ABSTRACT FROM AUTHOR]

Abstract We investigate the evolution of supply chain alliance networks with a focus on the influence of structural, firm-, and industry-level mechanisms. While several structural supply chain characteristics have been found to be significant drivers of firm innovation and performance, a dearth of studies exists examining how these characteristics change over time by influencing one another. We develop and empirically test hypotheses on the impact of prior structural configurations and the moderating roles of absorptive capacity and industry growth on the temporal trajectory of supply chain alliance network structures. Adopting a multi-method approach, we jointly use econometric analyses and simulation experiments to examine our hypotheses from complementary angles. Specifically, we characterize the dynamic relationship between the structural mechanisms on a longitudinal dataset of 2221 unique firms and 13,668 firm-year observations spanning 25 years. We find empirical support for negative crossover effects between two key structural properties of supply chain alliance networks, a positive moderation of a firm's absorptive capacity, and a negative moderation of industry growth on the structural reinforcement. We conduct corresponding simulation experiments based on a separable temporal exponential random graph model (STERGM) to track the temporal changes in the simulated networks' key measures. The simulation results concur with most of our empirical findings and provide additional insights complementary to our econometric analysis results. By focusing on the mechanism of temporal changes in network structural properties, our study contributes to supply chain management research with a supply network perspective and interfirm alliance network research by broadening its scope into structural dynamism. Our multi-method approach demonstrates how multiple complementary methodologies can foster a more nuanced understanding of managing supply chain alliance network management. [ABSTRACT FROM AUTHOR]

Social recommender systems aim to support user preferences and help users make better decisions in social media. The social network and the social context are two vital elements in social recommender systems. In this contribution, we propose a new framework for a social recommender system based on both network structure analysis and social context mining. Exponential random graph models (ERGMs) are able to capture and simulate the complex structure of a micro-blog network. We derive the prediction formula from ERGMs for recommending micro-blog users. Then, a primary recommendation list is created by analysing the micro-blog network structure. In the next step, we calculate the sentiment similarities of micro-blog users based on a sentiment feature set which is extracted from users’ tweets. Sentiment similarities are used to filter the primary recommendation list and find users who have similar attitudes on the same topic. The goal of those two steps is to make the social recommender system much more precise and to satisfy users’ psychological preferences. At the end, we use this new framework deal with big real-world data. The recommendation results of diabetes accounts of Weibo show that our method outperforms other social recommender systems. [ABSTRACT FROM AUTHOR]

Whereas previous studies on teacher collaboration have focused almost exclusively on improving teaching, this paper investigates collaboration in three highly important school improvement areas. Data for three collaboration networks were collected in four secondary schools in Switzerland in 2018 on teachers exploring new ideas on teaching (teaching improvement), teamwork (team improvement), and school organization (organizational improvement). Using social network analysis, we examined to what extent the collaboration networks overlap, how network structures differ, and what factors explain these differences. The results revealed substantial differences between collaboration in the three areas. This suggests that future research should examine collaboration from a multidimensional network perspective. [ABSTRACT FROM AUTHOR]

The exponential random graph model (ERGM) is a well-established statistical approach to modelling social network data. However, Monte Carlo estimation of ERGM parameters is a computationally intensive procedure that imposes severe limits on the size of full networks that can be fitted. We demonstrate the use of snowball sampling and conditional estimation to estimate ERGM parameters for large networks, with the specific goal of studying the validity of inference about the presence of such effects as network closure and attribute homophily. We estimate parameters for snowball samples from the network in parallel, and combine the estimates with a meta-analysis procedure. We assess the accuracy of this method by applying it to simulated networks with known parameters, and also demonstrate its application to networks that are too large (over 40 000 nodes) to estimate social circuit and other more advanced ERGM specifications directly. We conclude that this approach offers reliable inference for closure and homophily. [ABSTRACT FROM AUTHOR]

Network sampling poses a radical idea: that it is possible to measure global network structure without the full population coverage assumed in most network studies. Network sampling is only useful, however, if a researcher can produce accurate global network estimates. This article explores the practicality of making network inference, focusing on the approach introduced in Smith (2012). The method uses sampled ego network data and simulation techniques to make inference about the global features of the true, unknown network. The validity check here includes more difficult scenarios than previous tests, including those that go beyond the initial scope conditions of the method. I examine networks with a skewed degree distribution and surveys that limit the number of social ties a respondent can list. For each network/survey combination, I take a random ego network sample, run the simulation method, and compare the results to the true values (using measures of connectivity and cohesion). I also test the method on local measures of network structure. The results, on the whole, are encouraging. The method produces good estimates even in cases where the degree distribution is skewed and the survey is strongly restricted. I also find that is it better to not truncate the survey if possible. If the survey must be restricted, the researcher would do well to infer the missing data, rather than use the raw data naively. [ABSTRACT FROM PUBLISHER]

Drawing on the 'local buzz and global pipeline' perspective on the geographies of production in knowledge-intensive industries, our analysis associates observed network patterns with exogenous city characteristics (e.g., GDP and population) and endogenous local network structures (e.g., 'star' network structures) through exponential random graph models ( ERGMs). Our analysis contributes to the quantitative analysis of urban systems and locational strategies of producer service firms, as we: ( i) perform a direct two-mode network analysis of intercity corporate networks; ( ii) explore the association between macro network patterns and local formation processes; and ( iii) identify regional dynamics in network formation. [ABSTRACT FROM AUTHOR]

This study examines how venture capital (VC) networks form when financing hospitality and tourism start-ups. We propose that networks form as a result of exogenous attributes along with endogenous dependencies, which have been largely under-studied in the hospitality and tourism finance literature. Using a purposefully developed dataset of 644 VC investment deals, this study employed exponential random graph models (ERGMs) to examine how homophily in VC investor-specific attributes influences the tie formation, and to model the likelihood of future tie formation in VC networks in terms of endogenous dependencies. Results reveal that homophily among VC investors increases the likelihood of tie formation, while the establishment of future ties shows a significant tendency of multiple triangulation and connectivity in VC networks. The study provides insights into entrepreneurial equity financing from the VC perspective. It advances the network research and methodology by revealing the underlying structural processes of network formation through ERGMs. • Examining venture capital (VC) investment in hospitality and tourism entrepreneurial finance. • Modeling endogenous dependencies along with exogenous attributes in VC networks. • Advancing network research and methodology with the application of exponential random graph models (ERGMs). • Homophily among VC investors increases the likelihood of tie formation. • The establishment of future ties shows significant tendency of multiple triangulation and connectivity in VC networks. [ABSTRACT FROM AUTHOR]

• Exponential random graph models (ERGMs) are a flexible class of network models. • We develop a novel ERGM-based Bayesian multilevel model for groups of brain networks. • Fitting networks simultaneously yields more precise estimates for model parameters. • Our method can also assess differences in network properties between multiple groups. • We demonstrate our method on resting-state fMRI data from Cam-CAN, an ageing study. The brain can be modelled as a network with nodes and edges derived from a range of imaging modalities: the nodes correspond to spatially distinct regions and the edges to the interactions between them. Whole-brain connectivity studies typically seek to determine how network properties change with a given categorical phenotype such as age-group, disease condition or mental state. To do so reliably, it is necessary to determine the features of the connectivity structure that are common across a group of brain scans. Given the complex interdependencies inherent in network data, this is not a straightforward task. Some studies construct a group-representative network (GRN), ignoring individual differences, while other studies analyse networks for each individual independently, ignoring information that is shared across individuals. We propose a Bayesian framework based on exponential random graph models (ERGM) extended to multiple networks to characterise the distribution of an entire population of networks. Using resting-state fMRI data from the Cam-CAN project, a study on healthy ageing, we demonstrate how our method can be used to characterise and compare the brain's functional connectivity structure across a group of young individuals and a group of old individuals. [ABSTRACT FROM AUTHOR]

As one of the fast-developing mega-city regions in the world, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is in the process of construction. Drawing on the idea of "cities as strategic places", our analysis describes the city network in the GBA and contributes to the quantitative analysis of the formation of city network, as we: (1) identify the network structure through the lens of APS firms' locational strategies within the GBA; (2) take a step further in exploring the influential factors affecting the formation of the city network using the exponential random graph model (ERGM), compared with the previous studies only focusing on the description of the city networks. Major findings include: (1) the connectivity among cities has enhanced in the GBA since 2014, indicating that there are closer relationships among the cities; (2) from the perspective of the node cities, polycentric structure has emerged, whereby the status of Guangzhou and Shenzhen in the city network has improved and they are gradually becoming the core cities in the GBA; (3) a hierarchical structure is detected in the city network in the GBA, including the core cities (Hong Kong, Guangzhou and Shenzhen), the semi-peripheral cities (Macao, Foshan, Dongguan and Zhongshan), and the peripheral cities (Zhuhai, Jiangmen, Huizhou and Zhaoqing); (4) according to the results of ERGM, the city network in the GBA has significant "path dependence" characteristics, the existing city network, city's per capita GDP, innovative environment and international reputation are the main factors affecting the formation of the city network. • As the national strategic plan, the GBA is getting more and more attention in the worldwide. • The city network has been more intensive with the emerged polycentric pattern in the GBA since 2014. • Examine network structural characteristics by analyzing top 1, top 2 network and the sub-networks for different industries. • Identifying the determinant factors of the city network using ERGM. [ABSTRACT FROM AUTHOR]