Pub Date : 2025-03-11DOI: 10.1016/j.socnet.2025.03.001
Niles Breuer, Martina Baradel
This paper investigates the structure of the yakuza – the Japanese mafia – and the patterns of violence between local yakuza groups using a novel dataset of yakuza-on-yakuza conflict throughout Japan between 2014 and 2019. We define new multilevel temporal reciprocity measures and apply a multilevel exponential random graph model to investigate the structure of yakuza violence. We find low levels of retaliation and complex ‘cascading’ conflict structures and that yakuza syndicates act as cohesive organizations that can constrain the actions of their member groups. This research contributes to the understanding of the yakuza’s structure and how violent conflict occurs within organized crime groups.
{"title":"Investigating the dynamics of yakuza violence using multilevel network analysis","authors":"Niles Breuer, Martina Baradel","doi":"10.1016/j.socnet.2025.03.001","DOIUrl":"10.1016/j.socnet.2025.03.001","url":null,"abstract":"<div><div>This paper investigates the structure of the yakuza – the Japanese mafia – and the patterns of violence between local yakuza groups using a novel dataset of yakuza-on-yakuza conflict throughout Japan between 2014 and 2019. We define new multilevel temporal reciprocity measures and apply a multilevel exponential random graph model to investigate the structure of yakuza violence. We find low levels of retaliation and complex ‘cascading’ conflict structures and that yakuza syndicates act as cohesive organizations that can constrain the actions of their member groups. This research contributes to the understanding of the yakuza’s structure and how violent conflict occurs within organized crime groups.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"82 ","pages":"Pages 14-26"},"PeriodicalIF":2.9,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-14DOI: 10.1016/j.socnet.2025.02.001
Mélina Girard, David Décary-Hétu
This study examines the influence of social networks on the severity of sanctions in an online hacking forum, using a leaked dataset containing private interactions, reputation points, and administrative actions. Applying social identity theory, power structure, and social capital concepts to social network analysis, we find that members who committed spam, lacked bidirectional relationships with admins, and were less integrated and influential were more likely to be banned than warned. Our findings highlight the significant role of social ties and individual behaviors in determining sanctions, offering new insights into the dynamics of illicit online communities.
{"title":"From warnings to bans: The role of social networks in the severity of sanctions","authors":"Mélina Girard, David Décary-Hétu","doi":"10.1016/j.socnet.2025.02.001","DOIUrl":"10.1016/j.socnet.2025.02.001","url":null,"abstract":"<div><div>This study examines the influence of social networks on the severity of sanctions in an online hacking forum, using a leaked dataset containing private interactions, reputation points, and administrative actions. Applying social identity theory, power structure, and social capital concepts to social network analysis, we find that members who committed spam, lacked bidirectional relationships with admins, and were less integrated and influential were more likely to be banned than warned. Our findings highlight the significant role of social ties and individual behaviors in determining sanctions, offering new insights into the dynamics of illicit online communities.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"82 ","pages":"Pages 1-13"},"PeriodicalIF":2.9,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10DOI: 10.1016/j.socnet.2024.12.002
Judith Gilsbach , Johannes Stauder
This study examines the network evolution among sociology freshmen students during and after the Covid-19 pandemic as a natural experiment on the impacts of digitalised communication. The first surveyed cohort (N = 42) began their studies under lockdown in October 2020, when all classes were taught online (lockdown cohort). The second cohort (N = 66) started one year later when the lockdown measures were released partly and most classes were taught in a hybrid mode (hybrid cohort). We use Stochastic Actor-Oriented Models (SAOM) for model estimation; missing relations due to actor non-response are multiply imputed using SAOM-based procedures. The findings show (1) that the network among students of the lockdown cohort developed slower and reached a lower density at the end of the first term, (2) that the probability of triadic closure was significantly lower in the lockdown than in the hybrid cohort and (3) that in both cohorts, students have a stronger tendency to get acquainted if they share classes, but (4) that shared classes were more important for tie formation during lockdown. We conclude that digital communication will mitigate the opportunities to make new acquaintances and friends.
{"title":"Digital communication and tie formation amongst freshmen students during and after the pandemic","authors":"Judith Gilsbach , Johannes Stauder","doi":"10.1016/j.socnet.2024.12.002","DOIUrl":"10.1016/j.socnet.2024.12.002","url":null,"abstract":"<div><div>This study examines the network evolution among sociology freshmen students during and after the Covid-19 pandemic as a natural experiment on the impacts of digitalised communication. The first surveyed cohort (N = 42) began their studies under lockdown in October 2020, when all classes were taught online (lockdown cohort). The second cohort (N = 66) started one year later when the lockdown measures were released partly and most classes were taught in a hybrid mode (hybrid cohort). We use Stochastic Actor-Oriented Models (SAOM) for model estimation; missing relations due to actor non-response are multiply imputed using SAOM-based procedures. The findings show (1) that the network among students of the lockdown cohort developed slower and reached a lower density at the end of the first term, (2) that the probability of triadic closure was significantly lower in the lockdown than in the hybrid cohort and (3) that in both cohorts, students have a stronger tendency to get acquainted if they share classes, but (4) that shared classes were more important for tie formation during lockdown. We conclude that digital communication will mitigate the opportunities to make new acquaintances and friends.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"81 ","pages":"Pages 53-66"},"PeriodicalIF":2.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-19DOI: 10.1016/j.socnet.2024.12.001
Christopher McCarty , Peter D. Killworth
{"title":"Corrigendum to “Impact of methods for reducing respondent burden on personal network structural measures” [Soc. Netw. 29 (2007) 300–315]","authors":"Christopher McCarty , Peter D. Killworth","doi":"10.1016/j.socnet.2024.12.001","DOIUrl":"10.1016/j.socnet.2024.12.001","url":null,"abstract":"","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"81 ","pages":"Page 52"},"PeriodicalIF":2.9,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-16DOI: 10.1016/j.socnet.2024.11.002
José Luis Estévez , Carl Nordlund
Borgatti and Everett's model (2000) remains the prevailing standard for identifying categorical core-periphery structures in empirical networks, yet this method poses two significant issues. The first concerns the handling of inter-categorical ties—those linking core and periphery actors. The second problem is the model's definition of the ideal core as a complete block or clique, which can be overly stringent in practical applications. Building on advancements in direct blockmodeling, we propose modifications to address these shortcomings. To better handle inter-categorical ties, we replace the traditional cell-wise correlation approach with one based on exact- and minimum-density blocks. To relax the constraint of a fully connected core, we introduce the p-core, a proportional adaptation of the k-core/k-plex cohesive subgroups, providing greater flexibility in defining the level of cohesion required for core membership. We illustrate the advantages of these enhancements using both classic network examples and synthetic networks.
{"title":"Revising the Borgatti-Everett core-periphery model: Inter-categorical density blocks and partially connected cores","authors":"José Luis Estévez , Carl Nordlund","doi":"10.1016/j.socnet.2024.11.002","DOIUrl":"10.1016/j.socnet.2024.11.002","url":null,"abstract":"<div><div>Borgatti and Everett's model (2000) remains the prevailing standard for identifying categorical core-periphery structures in empirical networks, yet this method poses two significant issues. The first concerns the handling of inter-categorical ties—those linking core and periphery actors. The second problem is the model's definition of the ideal core as a complete block or clique, which can be overly stringent in practical applications. Building on advancements in direct blockmodeling, we propose modifications to address these shortcomings. To better handle inter-categorical ties, we replace the traditional cell-wise correlation approach with one based on exact- and minimum-density blocks. To relax the constraint of a fully connected core, we introduce the p-core, a proportional adaptation of the k-core/k-plex cohesive subgroups, providing greater flexibility in defining the level of cohesion required for core membership. We illustrate the advantages of these enhancements using both classic network examples and synthetic networks.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"81 ","pages":"Pages 31-51"},"PeriodicalIF":2.9,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-28DOI: 10.1016/j.socnet.2024.10.002
Eugene T.Y. Ang , Prasanta Bhattacharya , Andrew E.B. Lim
Evaluating the impact of policy interventions on respondents who are embedded in a social network is often challenging due to the presence of network interference within the treatment groups, as well as between treatment and non-treatment groups. In this paper, we propose a novel empirical strategy that combines network sampling based on the identification of independent sets with a stochastic actor-oriented model (SAOM) to infer the direct and net effects of a policy. By assigning respondents from an independent set to the treatment, we are able to block direct spillover of the treatment among the treated respondents for an extended period of time, during which the direct effect of the treatment can be isolated from the associated network interference. We empirically demonstrate this using a simulation-based evaluation of a fictitious policy implementation using both real-life and generated networks, and use a counterfactual approach to estimate the treatment effect of the policy. Our results highlight the effectiveness of our proposed empirical strategy, and notably, the role of network sampling techniques in influencing the evaluation of policy effects. The findings from this study have the potential to help researchers and policymakers with planning, designing, and anticipating policy responses in a networked society.
{"title":"Estimating policy effects in a social network with independent set sampling","authors":"Eugene T.Y. Ang , Prasanta Bhattacharya , Andrew E.B. Lim","doi":"10.1016/j.socnet.2024.10.002","DOIUrl":"10.1016/j.socnet.2024.10.002","url":null,"abstract":"<div><div>Evaluating the impact of policy interventions on respondents who are embedded in a social network is often challenging due to the presence of network interference within the treatment groups, as well as between treatment and non-treatment groups. In this paper, we propose a novel empirical strategy that combines network sampling based on the identification of independent sets with a stochastic actor-oriented model (SAOM) to infer the direct and net effects of a policy. By assigning respondents from an independent set to the treatment, we are able to block direct spillover of the treatment among the treated respondents for an extended period of time, during which the direct effect of the treatment can be isolated from the associated network interference. We empirically demonstrate this using a simulation-based evaluation of a fictitious policy implementation using both real-life and generated networks, and use a counterfactual approach to estimate the treatment effect of the policy. Our results highlight the effectiveness of our proposed empirical strategy, and notably, the role of network sampling techniques in influencing the evaluation of policy effects. The findings from this study have the potential to help researchers and policymakers with planning, designing, and anticipating policy responses in a networked society.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"81 ","pages":"Pages 17-30"},"PeriodicalIF":2.9,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-16DOI: 10.1016/j.socnet.2024.11.001
Andrea Fronzetti Colladon , Maurizio Naldi
This paper responds to a commentary by Neal (2024) regarding the Distinctiveness centrality metrics introduced by Fronzetti Colladon and Naldi (2020). Distinctiveness centrality offers a novel reinterpretation of degree centrality, particularly emphasizing the significance of direct connections to loosely connected peers within (social) networks. This response paper presents a more comprehensive analysis of the correlation between Distinctiveness and the Beta and Gamma measures. All five Distinctiveness measures are considered, as well as a more meaningful range of the α parameter and different network topologies, distinguishing between weighted and unweighted networks. Findings indicate significant variability in correlations, supporting the viability of Distinctiveness as alternative or complementary metrics within social network analysis. Moreover, the paper presents computational complexity analysis and simplified R code for practical implementation. Encouraging initial findings suggest potential applications in diverse domains, inviting further exploration and comparative analyses.
{"title":"Why distinctiveness centrality is distinctive","authors":"Andrea Fronzetti Colladon , Maurizio Naldi","doi":"10.1016/j.socnet.2024.11.001","DOIUrl":"10.1016/j.socnet.2024.11.001","url":null,"abstract":"<div><div>This paper responds to a commentary by Neal (2024) regarding the Distinctiveness centrality metrics introduced by Fronzetti Colladon and Naldi (2020). Distinctiveness centrality offers a novel reinterpretation of degree centrality, particularly emphasizing the significance of direct connections to loosely connected peers within (social) networks. This response paper presents a more comprehensive analysis of the correlation between Distinctiveness and the Beta and Gamma measures. All five Distinctiveness measures are considered, as well as a more meaningful range of the α parameter and different network topologies, distinguishing between weighted and unweighted networks. Findings indicate significant variability in correlations, supporting the viability of Distinctiveness as alternative or complementary metrics within social network analysis. Moreover, the paper presents computational complexity analysis and simplified R code for practical implementation. Encouraging initial findings suggest potential applications in diverse domains, inviting further exploration and comparative analyses.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"81 ","pages":"Pages 1-16"},"PeriodicalIF":2.9,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1016/j.socnet.2024.10.001
Jennifer Watling Neal
This pre-registered meta-analysis uses multi-level random effects models to give precise estimates of average outbound best friend and friend nominations – average outdegree centrality – in youth friendship networks and examines whether average outdegree centrality varies by age, gender, and geographic region. Pooling 196 estimates reported in 51 articles reflecting 37 datasets from whole network studies, youth nominated 4.80 best friends on average (). Additionally, pooling 64 estimates reported in 20 articles reflecting 18 datasets from whole network studies, youth nominated 6.05 friends on average (). Early adolescents (10–14 years) nominated significantly more best friends than adolescents (15–18 years). However, there were no significant differences in average outdegree centrality by the gender or geographic region of the sample. Findings provide future research directions for understanding youth socializing environments and implications for peer interventions.
{"title":"How many friends do youth nominate? A meta-analysis of gender, age, and geographic differences in average outdegree centrality","authors":"Jennifer Watling Neal","doi":"10.1016/j.socnet.2024.10.001","DOIUrl":"10.1016/j.socnet.2024.10.001","url":null,"abstract":"<div><div>This pre-registered meta-analysis uses multi-level random effects models to give precise estimates of average outbound best friend and friend nominations – average outdegree centrality – in youth friendship networks and examines whether average outdegree centrality varies by age, gender, and geographic region. Pooling 196 estimates reported in 51 articles reflecting 37 datasets from whole network studies, youth nominated 4.80 best friends on average (<span><math><mrow><mi>S</mi><mi>E</mi><mo>=</mo><mo>.</mo><mn>37</mn></mrow></math></span>). Additionally, pooling 64 estimates reported in 20 articles reflecting 18 datasets from whole network studies, youth nominated 6.05 friends on average (<span><math><mrow><mi>S</mi><mi>E</mi><mo>=</mo><mo>.</mo><mn>60</mn></mrow></math></span>). Early adolescents (10–14 years) nominated significantly more best friends than adolescents (15–18 years). However, there were no significant differences in average outdegree centrality by the gender or geographic region of the sample. Findings provide future research directions for understanding youth socializing environments and implications for peer interventions.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"80 ","pages":"Pages 65-75"},"PeriodicalIF":2.9,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1016/j.socnet.2024.09.001
Zachary P. Neal
Statistical analysis of bipartite networks frequently requires randomly sampling from the set of all bipartite networks with the same degree sequence as an observed network. Trade algorithms offer an efficient way to generate samples of bipartite networks by incrementally ‘trading’ the positions of some of their edges. However, it is difficult to know how many such trades are required to ensure that the sample is random. I propose a stopping rule that focuses on the distance between sampled networks and the observed network, and stops performing trades when this distribution stabilizes. Analyses demonstrate that, for over 650 different degree sequences, using this stopping rule ensures a random sample with a high probability, and that it is practical for use in empirical applications.
{"title":"A stopping rule for randomly sampling bipartite networks with fixed degree sequences","authors":"Zachary P. Neal","doi":"10.1016/j.socnet.2024.09.001","DOIUrl":"10.1016/j.socnet.2024.09.001","url":null,"abstract":"<div><p>Statistical analysis of bipartite networks frequently requires randomly sampling from the set of all bipartite networks with the same degree sequence as an observed network. Trade algorithms offer an efficient way to generate samples of bipartite networks by incrementally ‘trading’ the positions of some of their edges. However, it is difficult to know how many such trades are required to ensure that the sample is random. I propose a stopping rule that focuses on the distance between sampled networks and the observed network, and stops performing trades when this distribution stabilizes. Analyses demonstrate that, for over 650 different degree sequences, using this stopping rule ensures a random sample with a high probability, and that it is practical for use in empirical applications.</p></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"80 ","pages":"Pages 59-64"},"PeriodicalIF":2.9,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378873324000583/pdfft?md5=22656573e4c61565c180edcf70d10157&pid=1-s2.0-S0378873324000583-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142162890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.1016/j.socnet.2024.08.007
C. Broccatelli , P. Nixon , P. Moss , S. Baggio , A. Young , D. Newcomb
The present empirical study aims to explore medical knowledge sharing in the Australian healthcare context, aiming to broadly evaluate the potential impact of Project ECHO®, an online mentoring and networking health program. We focus on health-related knowledge sharing practices among the network of professionals through formal and informal channels, and across different health and non-health sectors and organisational systems. Studying knowledge transmission among professional networks is essential for optimizing healthcare delivery, promoting innovation, and providing insights on improvement of patient experiences within the healthcare system. We utilize a multilevel approach to shape our data collection strategy. Employing network measures and Multilevel Exponential Random Graph Models, we aim to explore how advice and knowledge sharing behaviours among healthcare professionals and their institutions are interdependently connected. Then, we incorporate network generated results within an evaluation framework for establishing some aspects of the efficiency of the ECHO program along four pillars: Acceptability, Capability, Reachability, and Integration. Our investigation found that among ECHO members, hierarchy is less pronounced compared to across levels and organizations, with certain individuals emerging as central in advice-sharing. The multilevel network perspective showed complex, informal patterns of knowledge and information sharing, including inter-organizational hierarchy, role and sector homophily, brokerage roles with popularity across health organizations, and connectivity through knowledge-sharing in cross-level small group clusters.
{"title":"Multilevel integrated healthcare: The evaluation of Project ECHO® networks to integrate children’s healthcare in Australia","authors":"C. Broccatelli , P. Nixon , P. Moss , S. Baggio , A. Young , D. Newcomb","doi":"10.1016/j.socnet.2024.08.007","DOIUrl":"10.1016/j.socnet.2024.08.007","url":null,"abstract":"<div><p>The present empirical study aims to explore medical knowledge sharing in the Australian healthcare context, aiming to broadly evaluate the potential impact of Project ECHO®, an online mentoring and networking health program. We focus on health-related knowledge sharing practices among the network of professionals through formal and informal channels, and across different health and non-health sectors and organisational systems. Studying knowledge transmission among professional networks is essential for optimizing healthcare delivery, promoting innovation, and providing insights on improvement of patient experiences within the healthcare system. We utilize a multilevel approach to shape our data collection strategy. Employing network measures and Multilevel Exponential Random Graph Models, we aim to explore how advice and knowledge sharing behaviours among healthcare professionals and their institutions are interdependently connected. Then, we incorporate network generated results within an evaluation framework for establishing some aspects of the efficiency of the ECHO program along four pillars: <em>Acceptability</em>, <em>Capability</em>, <em>Reachability</em>, and <em>Integration</em>. Our investigation found that among ECHO members, hierarchy is less pronounced compared to across levels and organizations, with certain individuals emerging as central in advice-sharing. The multilevel network perspective showed complex, informal patterns of knowledge and information sharing, including inter-organizational hierarchy, role and sector homophily, brokerage roles with popularity across health organizations, and connectivity through knowledge-sharing in cross-level small group clusters.</p></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"80 ","pages":"Pages 44-58"},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378873324000571/pdfft?md5=32414cf56b2f664ab7030fc6a513dfc4&pid=1-s2.0-S0378873324000571-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142162877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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