Pub Date : 2025-11-29DOI: 10.1016/j.socnet.2025.11.006
Lea Ellwardt , Theo G. van Tilburg
Objectives
Theorizations of social exchanges often assume that people prefer interactions with supportive others but avoid others they perceive as demanding or difficult. Yet, many social relationships are ambivalent, i.e., entail a combination of positive and negative interactions. We refer to difficult relationships as ties with an equal and high share of both elements or ties where the net balance of these two elements is outweighed by negativity. This study inquires how many difficult relationships prevail in the personal network in late life and where they originate.
Methods
Survey data are from the Longitudinal Aging Study Amsterdam (LASA) on egocentric networks. The sample comprised 892 respondents (mean age 73; range 61–100) and their 4273 network members. Models consist of mixed effects and logistic regressions for explaining difficulty on the relationship level and the network level.
Results
We found that 15 % of older adults engage in difficult relationships. The difficulty was most often found in involuntary relationships (with siblings, parents, neighbors), and relationships characterized by low receipt but high provision of emotional support, and high volatility. The difficulty in a personal network was more likely for older adults embedded in instable networks and networks that yielded difficult relationships among members in their network.
Discussion
People may face structural constraints that pressure them to continue engaging socially with others, even if they sometimes find them to be difficult. Research should take these ties seriously, as negative ties may bother more than positive ties benefit older adults.
{"title":"The ties that bother: Difficult relationships in the personal networks of older adults","authors":"Lea Ellwardt , Theo G. van Tilburg","doi":"10.1016/j.socnet.2025.11.006","DOIUrl":"10.1016/j.socnet.2025.11.006","url":null,"abstract":"<div><h3>Objectives</h3><div>Theorizations of social exchanges often assume that people prefer interactions with supportive others but avoid others they perceive as demanding or difficult. Yet, many social relationships are ambivalent, i.e., entail a combination of positive and negative interactions. We refer to difficult relationships as ties with an equal and high share of both elements or ties where the net balance of these two elements is outweighed by negativity. This study inquires how many difficult relationships prevail in the personal network in late life and where they originate.</div></div><div><h3>Methods</h3><div>Survey data are from the Longitudinal Aging Study Amsterdam (LASA) on egocentric networks. The sample comprised 892 respondents (mean age 73; range 61–100) and their 4273 network members. Models consist of mixed effects and logistic regressions for explaining difficulty on the relationship level and the network level.</div></div><div><h3>Results</h3><div>We found that 15 % of older adults engage in difficult relationships. The difficulty was most often found in involuntary relationships (with siblings, parents, neighbors), and relationships characterized by low receipt but high provision of emotional support, and high volatility. The difficulty in a personal network was more likely for older adults embedded in instable networks and networks that yielded difficult relationships among members in their network.</div></div><div><h3>Discussion</h3><div>People may face structural constraints that pressure them to continue engaging socially with others, even if they sometimes find them to be difficult. Research should take these ties seriously, as negative ties may bother more than positive ties benefit older adults.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"85 ","pages":"Pages 47-56"},"PeriodicalIF":2.4,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684390","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}
Community interventions increasingly leverage Social Network Analysis (SNA) both to understand relational patterns and to facilitate structural changes within networks. Indeed, SNA serves not only as an analytical tool but also as a catalyst for reflection and change. Although SNA has been widely used as an intervention tool, its application in cross-national contexts remains underexplored. This study aims to address this research gap by investigating how SNA can contribute to cross-national community interventions. We use a case study approach based on a longitudinal analysis of the Assistance and Legal Program for Emigrant Support (ALPES) network, a cross-national project established at the Italian-French border. In this project, SNA has been used both as a diagnostic tool to map the information exchange network of third-sector organizations and as a strategic intervention strategy that produced behavioral changes in these organizations. Our results show that SNA functioned as both a translational monitoring tool and a catalytic intervention: network visualization prompted organizations to strategically alter their collaborative patterns and address structural gaps in migrant support services across borders. This demonstrates how network feedback processes can enhance inter-organizational collaboration in complex cross-national contexts.
{"title":"From mapping to action: Social network analysis as a strategic tool in cross-national community interventions","authors":"Giorgia Trasciani , Stefano Ghinoi , Guido Conaldi","doi":"10.1016/j.socnet.2025.11.005","DOIUrl":"10.1016/j.socnet.2025.11.005","url":null,"abstract":"<div><div>Community interventions increasingly leverage Social Network Analysis (SNA) both to understand relational patterns and to facilitate structural changes within networks. Indeed, SNA serves not only as an analytical tool but also as a catalyst for reflection and change. Although SNA has been widely used as an intervention tool, its application in cross-national contexts remains underexplored. This study aims to address this research gap by investigating how SNA can contribute to cross-national community interventions. We use a case study approach based on a longitudinal analysis of the Assistance and Legal Program for Emigrant Support (ALPES) network, a cross-national project established at the Italian-French border. In this project, SNA has been used both as a diagnostic tool to map the information exchange network of third-sector organizations and as a strategic intervention strategy that produced behavioral changes in these organizations. Our results show that SNA functioned as both a translational monitoring tool and a catalytic intervention: network visualization prompted organizations to strategically alter their collaborative patterns and address structural gaps in migrant support services across borders. This demonstrates how network feedback processes can enhance inter-organizational collaboration in complex cross-national contexts.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"85 ","pages":"Pages 35-46"},"PeriodicalIF":2.4,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616710","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 : 2025-11-22DOI: 10.1016/j.socnet.2025.11.004
Shuyue Zhang , Linlin Lei , Lilan Liu , Shijiang Zuo
Structural features of social networks could influence interpersonal interactions, yet few studies have examined their link to social exclusion. This research investigated how relational mobility affects social exclusion behavior, with social participation intention as a mediator. We first developed the Social Exclusion Behavior Scale and demonstrated its reliability and validity in Study 1a (N = 1275). Using this scale, Study 1b (N = 650) found that relational mobility was negatively correlated with social exclusion behavior, and social participation intention played a mediating role. Study 2 (N = 209) manipulated relational mobility to clarify causal relationship and found that participants in the high-mobility group (vs. low-mobility) exhibited stronger social participation intentions and subsequently engaged in less social exclusion behavior. These findings identify relational mobility as a key structural feature of social networks influencing social exclusion behavior, clarify its underlying mechanism, and offer practical insights for interventions aimed at reducing social exclusion behavior.
{"title":"The influence of relational mobility on social exclusion behavior: The mediating role of social participation intention","authors":"Shuyue Zhang , Linlin Lei , Lilan Liu , Shijiang Zuo","doi":"10.1016/j.socnet.2025.11.004","DOIUrl":"10.1016/j.socnet.2025.11.004","url":null,"abstract":"<div><div>Structural features of social networks could influence interpersonal interactions, yet few studies have examined their link to social exclusion. This research investigated how relational mobility affects social exclusion behavior, with social participation intention as a mediator. We first developed the Social Exclusion Behavior Scale and demonstrated its reliability and validity in Study 1a (<em>N</em> = 1275). Using this scale, Study 1b (<em>N</em> = 650) found that relational mobility was negatively correlated with social exclusion behavior, and social participation intention played a mediating role. Study 2 (<em>N</em> = 209) manipulated relational mobility to clarify causal relationship and found that participants in the high-mobility group (vs. low-mobility) exhibited stronger social participation intentions and subsequently engaged in less social exclusion behavior. These findings identify relational mobility as a key structural feature of social networks influencing social exclusion behavior, clarify its underlying mechanism, and offer practical insights for interventions aimed at reducing social exclusion behavior.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"85 ","pages":"Pages 24-34"},"PeriodicalIF":2.4,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571193","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 : 2025-11-22DOI: 10.1016/j.socnet.2025.11.002
Isidro Maya-Jariego, Francisco J. Santolaya, Pablo Pastor-Alcayde
A neighborhood's psychological sense of community is shaped by residents’ rootedness and their participation in everyday activities such as walking or using green spaces. Having children, owning a home, or walking a dog are often positively associated with a stronger sense of belonging to the local environment. However, the social foundations of the subjective experience of belonging and emotional connection to one’s neighborhood remain underexplored. Moreover, relatively few empirical studies have examined multiple senses of community simultaneously. This study analyzes the relationship between the structural characteristics of personal networks and the psychological sense of community among residents of four neighborhoods in the historic center and one in the urban periphery of Seville, in southern Spain. In each case, we also compare the sense of belonging to the neighborhood and to the city. The community survey included 430 residents. Personal networks characterized by higher fragmentation and greater homophily were more common in the peripheral neighborhood, where residents also reported a comparatively lower sense of community than in the historic center. However, the strongest predictors of neighborhood- and city-level belonging were comparative perceived well-being, length of residence in the neighborhood, and the number of neighborhoods in which respondents had previously lived.
{"title":"Multiple senses of community in central and peripheral neighborhoods of seville: The fragmentation of personal networks in social housing estates","authors":"Isidro Maya-Jariego, Francisco J. Santolaya, Pablo Pastor-Alcayde","doi":"10.1016/j.socnet.2025.11.002","DOIUrl":"10.1016/j.socnet.2025.11.002","url":null,"abstract":"<div><div>A neighborhood's psychological sense of community is shaped by residents’ rootedness and their participation in everyday activities such as walking or using green spaces. Having children, owning a home, or walking a dog are often positively associated with a stronger sense of belonging to the local environment. However, the social foundations of the subjective experience of belonging and emotional connection to one’s neighborhood remain underexplored. Moreover, relatively few empirical studies have examined multiple senses of community simultaneously. This study analyzes the relationship between the structural characteristics of personal networks and the psychological sense of community among residents of four neighborhoods in the historic center and one in the urban periphery of Seville, in southern Spain. In each case, we also compare the sense of belonging to the neighborhood and to the city. The community survey included 430 residents. Personal networks characterized by higher fragmentation and greater homophily were more common in the peripheral neighborhood, where residents also reported a comparatively lower sense of community than in the historic center. However, the strongest predictors of neighborhood- and city-level belonging were comparative perceived well-being, length of residence in the neighborhood, and the number of neighborhoods in which respondents had previously lived.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"85 ","pages":"Pages 13-23"},"PeriodicalIF":2.4,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571194","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 : 2025-11-20DOI: 10.1016/j.socnet.2025.11.001
Minheng Chen , Yang-chih Fu , Xin Guo , Qiang Fu
The estimation and measurement of the size of egocentric networks have sparked vigorous discussion and debate. Drawing on datasets from the Taiwan Social Change Survey, this study explores methodological issues pertaining to the change of core networks in Taiwan from 1997 to 2017 via a modified Poisson mixture approach, assesses the efficiency of name generators as a survey instrument via Fisher Information Maximizer, and investigates the role of social desirability in reporting core networks. Net of other effects, the study finds that individuals expressing a strong sense of social desirability report significantly fewer close contacts and face a higher risk of social isolation. Name generators in this study are associated with trivial design errors and can yield estimates comparable to those produced by exact enumeration. These findings are situated in the drastic changes in face-to-face survey interviews as well as the cultural context of Taiwan and, more broadly, East Asia. They call for further research inquiries into methodological issues regarding measuring and estimating egocentric networks in a transnational and modern setting.
对自我中心网络大小的估计和测量引发了激烈的讨论和争论。本研究以台湾社会变迁调查资料为基础，运用修正的泊松混合方法，探讨1997 - 2017年台湾核心网路变迁的方法问题，运用Fisher Information Maximizer评估名称生成器作为调查工具的效率，并探讨社会期望度在核心网路报告中的作用。考虑到其他影响，研究发现，表现出强烈的社会渴望感的个人报告的亲密接触明显减少，面临更高的社会孤立风险。本研究中的名称生成器与微不足道的设计错误有关，并且可以产生与精确枚举产生的结果相当的估计。这些发现是基于面对面调查访谈的剧烈变化，以及台湾乃至更广泛的东亚的文化背景。他们呼吁对在跨国和现代环境中测量和估计自我中心网络的方法问题进行进一步的研究和调查。
{"title":"Social isolation by design: Bias in measuring core networks in Taiwan?","authors":"Minheng Chen , Yang-chih Fu , Xin Guo , Qiang Fu","doi":"10.1016/j.socnet.2025.11.001","DOIUrl":"10.1016/j.socnet.2025.11.001","url":null,"abstract":"<div><div>The estimation and measurement of the size of egocentric networks have sparked vigorous discussion and debate. Drawing on datasets from the Taiwan Social Change Survey, this study explores methodological issues pertaining to the change of core networks in Taiwan from 1997 to 2017 via a modified Poisson mixture approach, assesses the efficiency of name generators as a survey instrument via Fisher Information Maximizer, and investigates the role of social desirability in reporting core networks. Net of other effects, the study finds that individuals expressing a strong sense of social desirability report significantly fewer close contacts and face a higher risk of social isolation. Name generators in this study are associated with trivial design errors and can yield estimates comparable to those produced by exact enumeration. These findings are situated in the drastic changes in face-to-face survey interviews as well as the cultural context of Taiwan and, more broadly, East Asia. They call for further research inquiries into methodological issues regarding measuring and estimating egocentric networks in a transnational and modern setting.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"85 ","pages":"Pages 1-12"},"PeriodicalIF":2.4,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145555347","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 : 2025-11-06DOI: 10.1016/j.socnet.2025.10.003
Julie Riddell , Srebrenka Letina , Kathryn Skivington , Daniel Archambault , Valerie Wells , Emily Long , Ruth Hunter , Mark McCann , the MINI team
The methods used to study and intervene in networks have evolved over the past 90 years. Network research has gained strength from being an interdisciplinary field, but this has also meant that the innovations occurring within some disciplinary communities may not have disseminated to others. Disciplines focussing on health improvement may not have adopted innovations coming from other disciplines relevant to network science. Through a review of the literature, we focus on the key aspects which can inform community-based networks and health improvement (NHI) projects. The review aims to draw these innovations together to understand the range of methods currently available for NHI research and practice. We conducted a narrative synthesis of published literature that may be relevant for NHI projects, synthesising existing work on methods of network data collection, visualisation and intervention approaches.
Searches were conducted between the 8–11th January 2021, within the following databases; ACM Digital Library, EconLIt, ERIC, IEEE Explore, Medline, PsycInfo, Scopus, Social Sciences Citation Index and Sociological Abstracts. The expert community was also consulted to identify relevant research. Searches focused English language papers relating to methods for data collection, visualisation, and implementing interventions, but not statistical analysis. The search was not restricted to studies applied to health.
We used a systemic review methodology to identify peer-reviewed articles that met pre-defined inclusion criteria. Data extraction was restricted to 84 papers published since 2018, of which some were included within more than one category (Network Data collection N = 41, Network Visualisation N = 32, Network Intervention N = 30).
Analysis uncovered a diverse range of approaches to collecting, visualising and interventions using network data, and based on the included studies (rather than existing typologies) we developed a preliminary threefold typology of network and health improvement methods. We found nine types of network visualisation, eight types of data collection, and six types of network intervention approaches. Data visualisations commonly used node-link (circle-line) diagrams to visualise networks whilst key player interventions mostly used whole network data collection, and interventions using personal reflections of networks mostly used egonets. Visualisation was a feature of eight out of 30 intervention papers. Evaluation of the network methods was highly variable.
Our findings suggest potential areas for future methodological research around the use of network methods in community interventions, we propose further integration of data collection and visualisation approaches as part of intervention design, and encourage the network intervention community to integrate methods testing as part of their project to improve the evidence base for network methodology.
{"title":"Methods for interventions using networks to improve health: A narrative synthesis of methodological research on network data collection, visualisation and intervention","authors":"Julie Riddell , Srebrenka Letina , Kathryn Skivington , Daniel Archambault , Valerie Wells , Emily Long , Ruth Hunter , Mark McCann , the MINI team","doi":"10.1016/j.socnet.2025.10.003","DOIUrl":"10.1016/j.socnet.2025.10.003","url":null,"abstract":"<div><div>The methods used to study and intervene in networks have evolved over the past 90 years. Network research has gained strength from being an interdisciplinary field, but this has also meant that the innovations occurring within some disciplinary communities may not have disseminated to others. Disciplines focussing on health improvement may not have adopted innovations coming from other disciplines relevant to network science. Through a review of the literature, we focus on the key aspects which can inform community-based networks and health improvement (NHI) projects. The review aims to draw these innovations together to understand the range of methods currently available for NHI research and practice. We conducted a narrative synthesis of published literature that may be relevant for NHI projects, synthesising existing work on methods of network data collection, visualisation and intervention approaches.</div><div>Searches were conducted between the 8–11th January 2021, within the following databases; ACM Digital Library, EconLIt, ERIC, IEEE Explore, Medline, PsycInfo, Scopus, Social Sciences Citation Index and Sociological Abstracts. The expert community was also consulted to identify relevant research. Searches focused English language papers relating to methods for data collection, visualisation, and implementing interventions, but not statistical analysis. The search was not restricted to studies applied to health.</div><div>We used a systemic review methodology to identify peer-reviewed articles that met pre-defined inclusion criteria. Data extraction was restricted to 84 papers published since 2018, of which some were included within more than one category (Network Data collection N = 41, Network Visualisation N = 32, Network Intervention N = 30).</div><div>Analysis uncovered a diverse range of approaches to collecting, visualising and interventions using network data, and based on the included studies (rather than existing typologies) we developed a preliminary threefold typology of network and health improvement methods. We found nine types of network visualisation, eight types of data collection, and six types of network intervention approaches. Data visualisations commonly used node-link (circle-line) diagrams to visualise networks whilst key player interventions mostly used whole network data collection, and interventions using personal reflections of networks mostly used egonets. Visualisation was a feature of eight out of 30 intervention papers. Evaluation of the network methods was highly variable.</div><div>Our findings suggest potential areas for future methodological research around the use of network methods in community interventions, we propose further integration of data collection and visualisation approaches as part of intervention design, and encourage the network intervention community to integrate methods testing as part of their project to improve the evidence base for network methodology.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"84 ","pages":"Pages 202-219"},"PeriodicalIF":2.4,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465537","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 : 2025-10-27DOI: 10.1016/j.socnet.2025.10.002
Sean Everton, Seth Gray, Chad Machiela, Rob Schroeder
This paper aims to demonstrate the potential value of latent space network models (LSNMs) in supporting crisis managers during early community network intervention when information and resources are typically limited and the community is most vulnerable. Community network intervention requires crisis managers to identify existing ties between response network members and to foster and develop relations to eliminate (or minimize) bottlenecks and bridge gaps in human service coverage. We argue that in early crisis intervention, when responders have an incomplete understanding of the situation and limited resources, crisis managers may employ LSNMs to model relationships between actors who share parallel objectives (such as shelter volunteers or substance use disorder treatment specialists) and roles (such as law enforcement and emergency medical responders). Doing so would allow managers to compare the impact of proposed courses of action based on limited existing data and to guide the development of subnetworks to implement intervention initiatives. To demonstrate the utility of LSNMs, we examine the crisis response network that emerged following Oregon Governor Tina Kotek’s January 2024 state of emergency declaration concerning the fentanyl abuse in Portland, the state’s largest city. We find that LSNMs can assist early emergency responders with limited initial network data to model networks and identify critical limitations, assess risks associated with intervention strategies, and prioritize network development efforts to address shortcomings within available resources.
{"title":"Leveraging latent space network models for community intervention","authors":"Sean Everton, Seth Gray, Chad Machiela, Rob Schroeder","doi":"10.1016/j.socnet.2025.10.002","DOIUrl":"10.1016/j.socnet.2025.10.002","url":null,"abstract":"<div><div>This paper aims to demonstrate the potential value of latent space network models (LSNMs) in supporting crisis managers during early community network intervention when information and resources are typically limited and the community is most vulnerable. Community network intervention requires crisis managers to identify existing ties between response network members and to foster and develop relations to eliminate (or minimize) bottlenecks and bridge gaps in human service coverage. We argue that in early crisis intervention, when responders have an incomplete understanding of the situation and limited resources, crisis managers may employ LSNMs to model relationships between actors who share parallel objectives (such as shelter volunteers or substance use disorder treatment specialists) and roles (such as law enforcement and emergency medical responders). Doing so would allow managers to compare the impact of proposed courses of action based on limited existing data and to guide the development of subnetworks to implement intervention initiatives. To demonstrate the utility of LSNMs, we examine the crisis response network that emerged following Oregon Governor Tina Kotek’s January 2024 state of emergency declaration concerning the fentanyl abuse in Portland, the state’s largest city. We find that LSNMs can assist early emergency responders with limited initial network data to model networks and identify critical limitations, assess risks associated with intervention strategies, and prioritize network development efforts to address shortcomings within available resources.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"84 ","pages":"Pages 191-201"},"PeriodicalIF":2.4,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415548","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 : 2025-10-15DOI: 10.1016/j.socnet.2025.10.001
Daniel Cowen , Tobias Stark , Vincenz Frey , Andreas Flache
People become friends with one another primarily due to things they have in common, like shared demographic characteristics or shared interests. But on what similarities are people becoming friends at different stages of knowing one another? To study this, we use a longitudinal dataset that followed a cohort of students of one study programme at a Swiss technical university. We model how demographic traits of nationality and gender, and less observable interest-related traits of being social, a partygoer, a smart and hard-working student, contribute to friendships. Using Stochastic Actor- Oriented Models, we find a baseline level of both demographic and interest-related friendship homophily, indicating that there are differing reasons behind friendships. We also find that homophily based on demographic traits diminishes over time when students get to know those in their study cohort better. This suggest that homophily on observable traits is mainly relevant when people first meet but becomes less important over time.
{"title":"Trends of Friends – Time dynamics of Surface- and Deep- level traits in friendship formation and maintenance","authors":"Daniel Cowen , Tobias Stark , Vincenz Frey , Andreas Flache","doi":"10.1016/j.socnet.2025.10.001","DOIUrl":"10.1016/j.socnet.2025.10.001","url":null,"abstract":"<div><div>People become friends with one another primarily due to things they have in common, like shared demographic characteristics or shared interests. But on what similarities are people becoming friends at different stages of knowing one another? To study this, we use a longitudinal dataset that followed a cohort of students of one study programme at a Swiss technical university. We model how demographic traits of nationality and gender, and less observable interest-related traits of being social, a partygoer, a smart and hard-working student, contribute to friendships. Using Stochastic Actor- Oriented Models, we find a baseline level of both demographic and interest-related friendship homophily, indicating that there are differing reasons behind friendships. We also find that homophily based on demographic traits diminishes over time when students get to know those in their study cohort better. This suggest that homophily on observable traits is mainly relevant when people first meet but becomes less important over time.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"84 ","pages":"Pages 180-190"},"PeriodicalIF":2.4,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323688","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 : 2025-10-04DOI: 10.1016/j.socnet.2025.09.004
Yunsub Lee , Xinwei Xu
Aggregated relational data (ARD) provides valuable information for inferring structural features of personal social networks at scale. Following recent ARD studies, we suggest a formal parameter for agent-based modeling (ABM) that helps reflect multiple structural features of extended social networks (e.g., size; variation; distribution) and apply it to a widely known classic ABM—Axelrod’s cultural dynamic model. Results show that when incorporating realistic network features estimated from ARD, the model generates outcomes substantially different from its original results. Our study highlights ARD's potential to enrich ABM in reflecting more realistic networks that better connect micro-processes with macro-phenomena.
{"title":"Use of aggregated relational data in agent-based modeling","authors":"Yunsub Lee , Xinwei Xu","doi":"10.1016/j.socnet.2025.09.004","DOIUrl":"10.1016/j.socnet.2025.09.004","url":null,"abstract":"<div><div>Aggregated relational data (ARD) provides valuable information for inferring structural features of personal social networks at scale. Following recent ARD studies, we suggest a formal parameter for agent-based modeling (ABM) that helps reflect multiple structural features of extended social networks (e.g., size; variation; distribution) and apply it to a widely known classic ABM—Axelrod’s cultural dynamic model. Results show that when incorporating realistic network features estimated from ARD, the model generates outcomes substantially different from its original results. Our study highlights ARD's potential to enrich ABM in reflecting more realistic networks that better connect micro-processes with macro-phenomena.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"84 ","pages":"Pages 164-179"},"PeriodicalIF":2.4,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220009","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 : 2025-09-27DOI: 10.1016/j.socnet.2025.09.003
Jonathan Januar , H. Colin Gallagher , Johan Koskinen
The clandestine nature of covert networks makes reliable data difficult to obtain and leads to concerns with missing data. We explore the use of network models to represent missingness mechanisms. Exponential random graph models provide a flexible way of parameterising departures from conventional missingness assumptions and data management practices. We demonstrate the effects of model specification, true network structure, and different not-at-random missingness mechanisms across six empirical covert networks. Our framework for modelling realistic missingness mechanisms investigates potential inferential pitfalls, evaluates decisions in collecting data, and offers the opportunity to incorporate non-random missingness into the estimation of network generating mechanisms.
{"title":"In the shadow of silence: Modelling missing data in the dark networks of crime and terrorists","authors":"Jonathan Januar , H. Colin Gallagher , Johan Koskinen","doi":"10.1016/j.socnet.2025.09.003","DOIUrl":"10.1016/j.socnet.2025.09.003","url":null,"abstract":"<div><div>The clandestine nature of covert networks makes reliable data difficult to obtain and leads to concerns with missing data. We explore the use of network models to represent missingness mechanisms. Exponential random graph models provide a flexible way of parameterising departures from conventional missingness assumptions and data management practices. We demonstrate the effects of model specification, true network structure, and different not-at-random missingness mechanisms across six empirical covert networks. Our framework for modelling realistic missingness mechanisms investigates potential inferential pitfalls, evaluates decisions in collecting data, and offers the opportunity to incorporate non-random missingness into the estimation of network generating mechanisms.</div></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"84 ","pages":"Pages 147-163"},"PeriodicalIF":2.4,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157542","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}
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