This special issue of Network Science contains a collection of extended papers from the 8th International Conference on Complex Networks & their Applications (COMPLEX NETWORKS 2019) . This major international event in network science brings together every year researchers from around the globe. The great diversity of the participants’ scientific backgrounds ranges from Finance and Economics, Medicine and Neuroscience, Biology and Earth Sciences, Sociology and Political Science to Mathematics and Computer Science, Physics, and many others, making it a special opportunity to review the current state of the field and formulate new directions. This edition of the conference took place at the Calouste Gulbenkian Foundation in Lisbon (Portugal) from December 10 to December 12, 2019. It attracted 470 submissions with authors from 58 countries all over the world. After thorough review, 161 papers were selected to be included in the proceedings Cherifi et al. (2020a,b). The conference program also included keynote presentations from Lada Adamic (Facebook, Inc., USA), Reka Albert (Pennsylvania State University, USA), Ulrik Brandes (ETH Zurich, Switzerland), Stefan Thurner (Medical University of Vienna, Austria), Jari Saramki (Aalto University, Finland), and Michalis Vazirgiannis (LIX, cole Polytechnique, France). Papers invited for this special issue have been selected from the accepted contributions based on relevance to the journal and excellent reviews of the conference version of the papers. The authors were asked to submit an extended version of their conference submission for journal publication in accordance with the customary practice of adding 30% new material. These submissions went through the standard double-blind review process dictated by the journal guidelines. The seven papers accepted to this special issue provide a remarkable sample illustrating the diversity of issues studied in network science research.
{"title":"Introduction to the special issue on COMPLEX NETWORKS 2019","authors":"H. Cherifi, Luis M. Rocha","doi":"10.1017/nws.2021.8","DOIUrl":"https://doi.org/10.1017/nws.2021.8","url":null,"abstract":"This special issue of Network Science contains a collection of extended papers from the 8th International Conference on Complex Networks & their Applications (COMPLEX NETWORKS 2019) . This major international event in network science brings together every year researchers from around the globe. The great diversity of the participants’ scientific backgrounds ranges from Finance and Economics, Medicine and Neuroscience, Biology and Earth Sciences, Sociology and Political Science to Mathematics and Computer Science, Physics, and many others, making it a special opportunity to review the current state of the field and formulate new directions. This edition of the conference took place at the Calouste Gulbenkian Foundation in Lisbon (Portugal) from December 10 to December 12, 2019. It attracted 470 submissions with authors from 58 countries all over the world. After thorough review, 161 papers were selected to be included in the proceedings Cherifi et al. (2020a,b). The conference program also included keynote presentations from Lada Adamic (Facebook, Inc., USA), Reka Albert (Pennsylvania State University, USA), Ulrik Brandes (ETH Zurich, Switzerland), Stefan Thurner (Medical University of Vienna, Austria), Jari Saramki (Aalto University, Finland), and Michalis Vazirgiannis (LIX, cole Polytechnique, France). Papers invited for this special issue have been selected from the accepted contributions based on relevance to the journal and excellent reviews of the conference version of the papers. The authors were asked to submit an extended version of their conference submission for journal publication in accordance with the customary practice of adding 30% new material. These submissions went through the standard double-blind review process dictated by the journal guidelines. The seven papers accepted to this special issue provide a remarkable sample illustrating the diversity of issues studied in network science research.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42485168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Reciprocity—the mutual provisioning of support/goods—is a pervasive feature of social life. Directed networks provide a way to examine the structure of reciprocity in a community. However, measuring social networks involves assumptions about what relationships matter and how to elicit them, which may impact observed reciprocity. In particular, the practice of aggregating multiple sources of data on the same relationship (e.g., “double-sampled” data, where both the “giver” and “receiver” are asked to report on their relationship) may have pronounced impacts on network structure. To investigate these issues, we examine concordance (ties reported by both parties) and reciprocity in a set of directed, double-sampled social support networks. We find low concordance in people’s responses. Taking either the union (including any reported ties) or the intersection (including only concordant ties) of double-sampled relationships results in dramatically higher levels of reciprocity. Using multilevel exponential random graph models of social support networks from 75 villages in India, we show that these changes cannot be fully explained by the increase in the number of ties produced by layer aggregation. Respondents’ tendency to name the same people as both givers and receivers of support plays an important role, but this tendency varies across contexts and relationships type. We argue that no single method should necessarily be seen as the “correct” choice for aggregation of multiple sources of data on a single relationship type. Methods of aggregation should depend on the research question, the context, and the relationship in question.
{"title":"Measuring reciprocity: Double sampling, concordance, and network construction","authors":"Elspeth Ready, E. Power","doi":"10.1017/nws.2021.18","DOIUrl":"https://doi.org/10.1017/nws.2021.18","url":null,"abstract":"Abstract Reciprocity—the mutual provisioning of support/goods—is a pervasive feature of social life. Directed networks provide a way to examine the structure of reciprocity in a community. However, measuring social networks involves assumptions about what relationships matter and how to elicit them, which may impact observed reciprocity. In particular, the practice of aggregating multiple sources of data on the same relationship (e.g., “double-sampled” data, where both the “giver” and “receiver” are asked to report on their relationship) may have pronounced impacts on network structure. To investigate these issues, we examine concordance (ties reported by both parties) and reciprocity in a set of directed, double-sampled social support networks. We find low concordance in people’s responses. Taking either the union (including any reported ties) or the intersection (including only concordant ties) of double-sampled relationships results in dramatically higher levels of reciprocity. Using multilevel exponential random graph models of social support networks from 75 villages in India, we show that these changes cannot be fully explained by the increase in the number of ties produced by layer aggregation. Respondents’ tendency to name the same people as both givers and receivers of support plays an important role, but this tendency varies across contexts and relationships type. We argue that no single method should necessarily be seen as the “correct” choice for aggregation of multiple sources of data on a single relationship type. Methods of aggregation should depend on the research question, the context, and the relationship in question.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41369190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
original Articles A network approach to measuring state preferences max gallop and shahryar minhas 135 Artificial Benchmark for Community Detection (ABCD)—Fast random graph model with community structure bogumił kamiński, paweł prałat and françois théberge 153 Edge overlap in weighted and directed social networks heather mattie and jukka-pekka onnela 179 Functional disability and the role of children in U.S. older adults’ core discussion networks markus h. schafer and laura upenieks 194 The roles actors play in policy networks: Central positions in strongly institutionalized fields karin ingold, manuel fischer and dimitris christopoulos 213 A fused mixed-methods approach to thematic analysis of personal networks: Two case studies of caregiver support networks reza yousefi nooraie, bronwyn thompson, chelsea d’silva, ian zenlea, maryam tabatabaee and ardavan mohammad aghaei 236 network science editorial team
{"title":"NWS volume 9 issue 2 Cover and Back matter","authors":"Bronwyn Thompson","doi":"10.1017/nws.2021.7","DOIUrl":"https://doi.org/10.1017/nws.2021.7","url":null,"abstract":"original Articles A network approach to measuring state preferences max gallop and shahryar minhas 135 Artificial Benchmark for Community Detection (ABCD)—Fast random graph model with community structure bogumił kamiński, paweł prałat and françois théberge 153 Edge overlap in weighted and directed social networks heather mattie and jukka-pekka onnela 179 Functional disability and the role of children in U.S. older adults’ core discussion networks markus h. schafer and laura upenieks 194 The roles actors play in policy networks: Central positions in strongly institutionalized fields karin ingold, manuel fischer and dimitris christopoulos 213 A fused mixed-methods approach to thematic analysis of personal networks: Two case studies of caregiver support networks reza yousefi nooraie, bronwyn thompson, chelsea d’silva, ian zenlea, maryam tabatabaee and ardavan mohammad aghaei 236 network science editorial team","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2021.7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41438454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-01Epub Date: 2021-02-16DOI: 10.1017/nws.2020.49
Heather Mattie, Jukka-Pekka Onnela
With the increasing availability of behavioral data from diverse digital sources, such as social media sites and cell phones, it is now possible to obtain detailed information about the structure, strength, and directionality of social interactions in varied settings. While most metrics of network structure have traditionally been defined for unweighted and undirected networks only, the richness of current network data calls for extending these metrics to weighted and directed networks. One fundamental metric in social networks is edge overlap, the proportion of friends shared by two connected individuals. Here we extend definitions of edge overlap to weighted and directed networks, and present closed-form expressions for the mean and variance of each version for the Erdős-Rényi random graph and its weighted and directed counterparts. We apply these results to social network data collected in rural villages in southern Karnataka, India. We use our analytical results to quantify the extent to which the average overlap of the empirical social network deviates from that of corresponding random graphs and compare the values of overlap across networks. Our novel definitions allow the calculation of edge overlap for more complex networks and our derivations provide a statistically rigorous way for comparing edge overlap across networks.
{"title":"Edge Overlap in Weighted and Directed Social Networks.","authors":"Heather Mattie, Jukka-Pekka Onnela","doi":"10.1017/nws.2020.49","DOIUrl":"10.1017/nws.2020.49","url":null,"abstract":"<p><p>With the increasing availability of behavioral data from diverse digital sources, such as social media sites and cell phones, it is now possible to obtain detailed information about the structure, strength, and directionality of social interactions in varied settings. While most metrics of network structure have traditionally been defined for unweighted and undirected networks only, the richness of current network data calls for extending these metrics to weighted and directed networks. One fundamental metric in social networks is edge overlap, the proportion of friends shared by two connected individuals. Here we extend definitions of edge overlap to weighted and directed networks, and present closed-form expressions for the mean and variance of each version for the Erdős-Rényi random graph and its weighted and directed counterparts. We apply these results to social network data collected in rural villages in southern Karnataka, India. We use our analytical results to quantify the extent to which the average overlap of the empirical social network deviates from that of corresponding random graphs and compare the values of overlap across networks. Our novel definitions allow the calculation of edge overlap for more complex networks and our derivations provide a statistically rigorous way for comparing edge overlap across networks.</p>","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507499/pdf/nihms-1712396.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39519110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Dense networks with weighted connections often exhibit a community-like structure, where although most nodes are connected to each other, different patterns of edge weights may emerge depending on each node’s community membership. We propose a new framework for generating and estimating dense weighted networks with potentially different connectivity patterns across different communities. The proposed model relies on a particular class of functions which map individual node characteristics to the edges connecting those nodes, allowing for flexibility while requiring a small number of parameters relative to the number of edges. By leveraging the estimation techniques, we also develop a bootstrap methodology for generating new networks on the same set of vertices, which may be useful in circumstances where multiple data sets cannot be collected. Performance of these methods is analyzed in theory, simulations, and real data.
{"title":"Block dense weighted networks with augmented degree correction","authors":"Benjamin Leinwand, V. Pipiras","doi":"10.1017/nws.2022.23","DOIUrl":"https://doi.org/10.1017/nws.2022.23","url":null,"abstract":"Abstract Dense networks with weighted connections often exhibit a community-like structure, where although most nodes are connected to each other, different patterns of edge weights may emerge depending on each node’s community membership. We propose a new framework for generating and estimating dense weighted networks with potentially different connectivity patterns across different communities. The proposed model relies on a particular class of functions which map individual node characteristics to the edges connecting those nodes, allowing for flexibility while requiring a small number of parameters relative to the number of edges. By leveraging the estimation techniques, we also develop a bootstrap methodology for generating new networks on the same set of vertices, which may be useful in circumstances where multiple data sets cannot be collected. Performance of these methods is analyzed in theory, simulations, and real data.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43959518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Decentralized coordination is one of the fundamental challenges for societies and organizations. While extensively explored from a variety of perspectives, one issue that has received limited attention is human coordination in the presence of adversarial agents. We study this problem by situating human subjects as nodes on a network, and endowing each with a role, either regular (with the goal of achieving consensus among all regular players), or adversarial (aiming to prevent consensus among regular players). We show that adversarial nodes are, indeed, quite successful in preventing consensus. However, we demonstrate that having the ability to communicate among network neighbors can considerably improve coordination success, as well as resilience to adversarial nodes. Our analysis of communication suggests that adversarial nodes attempt to exploit this capability for their ends, but do so in a somewhat limited way, perhaps to prevent regular nodes from recognizing their intent. In addition, we show that the presence of trusted nodes generally has limited value, but does help when many adversarial nodes are present, and players can communicate. Finally, we use experimental data to develop computational models of human behavior and explore additional parametric variations: features of network topologies and densities, and placement, all using the resulting data-driven agent-based (DDAB) model.
{"title":"Robust coordination in adversarial social networks: From human behavior to agent-based modeling","authors":"Chen Hajaj, Zlatko Joveski, Sixie Yu, Yevgeniy Vorobeychik","doi":"10.1017/nws.2021.5","DOIUrl":"https://doi.org/10.1017/nws.2021.5","url":null,"abstract":"Abstract Decentralized coordination is one of the fundamental challenges for societies and organizations. While extensively explored from a variety of perspectives, one issue that has received limited attention is human coordination in the presence of adversarial agents. We study this problem by situating human subjects as nodes on a network, and endowing each with a role, either regular (with the goal of achieving consensus among all regular players), or adversarial (aiming to prevent consensus among regular players). We show that adversarial nodes are, indeed, quite successful in preventing consensus. However, we demonstrate that having the ability to communicate among network neighbors can considerably improve coordination success, as well as resilience to adversarial nodes. Our analysis of communication suggests that adversarial nodes attempt to exploit this capability for their ends, but do so in a somewhat limited way, perhaps to prevent regular nodes from recognizing their intent. In addition, we show that the presence of trusted nodes generally has limited value, but does help when many adversarial nodes are present, and players can communicate. Finally, we use experimental data to develop computational models of human behavior and explore additional parametric variations: features of network topologies and densities, and placement, all using the resulting data-driven agent-based (DDAB) model.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2021.5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47154475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We discuss a recently proposed family of statistical network models—relational hyperevent models (RHEMs)—for analyzing team selection and team performance in scientific coauthor networks. The underlying rationale for using RHEM in studies of coauthor networks is that scientific collaboration is intrinsically polyadic, that is, it typically involves teams of any size. Consequently, RHEM specify publication rates associated with hyperedges representing groups of scientists of any size. Going beyond previous work on RHEM for meeting data, we adapt this model family to settings in which relational hyperevents have a dedicated outcome, such as a scientific paper with a measurable impact (e.g., the received number of citations). Relational outcome can on the one hand be used to specify additional explanatory variables in RHEM since the probability of coauthoring may be influenced, for instance, by prior (shared) success of scientists. On the other hand, relational outcome can also serve as a response variable in models seeking to explain the performance of scientific teams. To tackle the latter, we propose relational hyperevent outcome models that are closely related with RHEM to the point that both model families can specify the likelihood of scientific collaboration—and the expected performance, respectively—with the same set of explanatory variables allowing to assess, for instance, whether variables leading to increased collaboration also tend to increase scientific impact. For illustration, we apply RHEM to empirical coauthor networks comprising more than 350,000 published papers by scientists working in three scientific disciplines. Our models explain scientific collaboration and impact by, among others, individual activity (preferential attachment), shared activity (familiarity), triadic closure, prior individual and shared success, and prior success disparity among the members of hyperedges.
{"title":"Micro-level network dynamics of scientific collaboration and impact: Relational hyperevent models for the analysis of coauthor networks","authors":"J. Lerner, Marian-Gabriel Hâncean","doi":"10.1017/nws.2022.29","DOIUrl":"https://doi.org/10.1017/nws.2022.29","url":null,"abstract":"Abstract We discuss a recently proposed family of statistical network models—relational hyperevent models (RHEMs)—for analyzing team selection and team performance in scientific coauthor networks. The underlying rationale for using RHEM in studies of coauthor networks is that scientific collaboration is intrinsically polyadic, that is, it typically involves teams of any size. Consequently, RHEM specify publication rates associated with hyperedges representing groups of scientists of any size. Going beyond previous work on RHEM for meeting data, we adapt this model family to settings in which relational hyperevents have a dedicated outcome, such as a scientific paper with a measurable impact (e.g., the received number of citations). Relational outcome can on the one hand be used to specify additional explanatory variables in RHEM since the probability of coauthoring may be influenced, for instance, by prior (shared) success of scientists. On the other hand, relational outcome can also serve as a response variable in models seeking to explain the performance of scientific teams. To tackle the latter, we propose relational hyperevent outcome models that are closely related with RHEM to the point that both model families can specify the likelihood of scientific collaboration—and the expected performance, respectively—with the same set of explanatory variables allowing to assess, for instance, whether variables leading to increased collaboration also tend to increase scientific impact. For illustration, we apply RHEM to empirical coauthor networks comprising more than 350,000 published papers by scientists working in three scientific disciplines. Our models explain scientific collaboration and impact by, among others, individual activity (preferential attachment), shared activity (familiarity), triadic closure, prior individual and shared success, and prior success disparity among the members of hyperedges.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48033553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reza Yousefi Nooraie, Bronwyn Thompson, Chelsea D'Silva, I. Zenlea, M. Tabatabaee, Ardavan Mohammad Aghaei
Abstract Thematic analysis of personal networks involves identifying regularities in network structure and content, and grouping networks into types/clusters, to allow for a holistic understanding of social complexities. We propose an inductive approach to network thematic analysis, applying the learnings from qualitative coding, fused mixed-methods analysis, and typology development. It involves framing (changing focus by magnifying, aggregating, and graphical configuration), pattern detection (identification of underlying dimensions, sorting, and clustering), labeling, and triangulating (confirmation and fine-tuning using quantitative and qualitative approaches); applied repeatedly and emergently. We describe this approach utilized in two cases of studying support networks of caregivers.
{"title":"A fused mixed-methods approach to thematic analysis of personal networks: Two case studies of caregiver support networks","authors":"Reza Yousefi Nooraie, Bronwyn Thompson, Chelsea D'Silva, I. Zenlea, M. Tabatabaee, Ardavan Mohammad Aghaei","doi":"10.1017/nws.2021.4","DOIUrl":"https://doi.org/10.1017/nws.2021.4","url":null,"abstract":"Abstract Thematic analysis of personal networks involves identifying regularities in network structure and content, and grouping networks into types/clusters, to allow for a holistic understanding of social complexities. We propose an inductive approach to network thematic analysis, applying the learnings from qualitative coding, fused mixed-methods analysis, and typology development. It involves framing (changing focus by magnifying, aggregating, and graphical configuration), pattern detection (identification of underlying dimensions, sorting, and clustering), labeling, and triangulating (confirmation and fine-tuning using quantitative and qualitative approaches); applied repeatedly and emergently. We describe this approach utilized in two cases of studying support networks of caregivers.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2021.4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41334640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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