F. Giroire, N. Nisse, Thibaud Trolliet, M. Sułkowska
Abstract Numerous works have been proposed to generate random graphs preserving the same properties as real-life large-scale networks. However, many real networks are better represented by hypergraphs. Few models for generating random hypergraphs exist, and also, just a few models allow to both preserve a power-law degree distribution and a high modularity indicating the presence of communities. We present a dynamic preferential attachment hypergraph model which features partition into communities. We prove that its degree distribution follows a power-law, and we give theoretical lower bounds for its modularity. We compare its characteristics with a real-life co-authorship network and show that our model achieves good performances. We believe that our hypergraph model will be an interesting tool that may be used in many research domains in order to reflect better real-life phenomena.
{"title":"Preferential attachment hypergraph with high modularity","authors":"F. Giroire, N. Nisse, Thibaud Trolliet, M. Sułkowska","doi":"10.1017/nws.2022.35","DOIUrl":"https://doi.org/10.1017/nws.2022.35","url":null,"abstract":"Abstract Numerous works have been proposed to generate random graphs preserving the same properties as real-life large-scale networks. However, many real networks are better represented by hypergraphs. Few models for generating random hypergraphs exist, and also, just a few models allow to both preserve a power-law degree distribution and a high modularity indicating the presence of communities. We present a dynamic preferential attachment hypergraph model which features partition into communities. We prove that its degree distribution follows a power-law, and we give theoretical lower bounds for its modularity. We compare its characteristics with a real-life co-authorship network and show that our model achieves good performances. We believe that our hypergraph model will be an interesting tool that may be used in many research domains in order to reflect better real-life phenomena.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"10 1","pages":"400 - 429"},"PeriodicalIF":1.7,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41379792","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 This study considered the role of adult children in the core networks of U.S. older adults with varying levels of functional health. Taking a multidimensional perspective of the ego network system, we considered (a) presence of child(ren) in the network, (b) contact with children network members, and (c) embeddedness of children within the network. We observed older parents from three waves of the National Social Life, Health, and Aging Project (NSHAP). The common ‘important matters’ name generator was used to construct egocentric network variables, while self-reported difficulty with activities of daily life was used to measure disablement transitions. Parameters were estimated with Generalized Estimating Equations (GEE). Though child turnover was common in parents’ core networks, there was no evidence linking disablement transitions to systematic forms of child reshuffling. Children that remained in parents’ networks, however, showed increased contact with parents and with other members of the network when the parent underwent disability progression. Disability onset was not significantly linked to either outcome. There was limited evidence of gender variation in these patterns. Overall, results strengthen the view that children are distinctive members of older adults’ core networks. Further, the role of adult children shifts most noticeably at advanced stages of the disablement process.
{"title":"Functional disability and the role of children in U.S. older adults’ core discussion networks","authors":"Markus H. Schafer, Laura Upenieks","doi":"10.1017/nws.2020.48","DOIUrl":"https://doi.org/10.1017/nws.2020.48","url":null,"abstract":"Abstract This study considered the role of adult children in the core networks of U.S. older adults with varying levels of functional health. Taking a multidimensional perspective of the ego network system, we considered (a) presence of child(ren) in the network, (b) contact with children network members, and (c) embeddedness of children within the network. We observed older parents from three waves of the National Social Life, Health, and Aging Project (NSHAP). The common ‘important matters’ name generator was used to construct egocentric network variables, while self-reported difficulty with activities of daily life was used to measure disablement transitions. Parameters were estimated with Generalized Estimating Equations (GEE). Though child turnover was common in parents’ core networks, there was no evidence linking disablement transitions to systematic forms of child reshuffling. Children that remained in parents’ networks, however, showed increased contact with parents and with other members of the network when the parent underwent disability progression. Disability onset was not significantly linked to either outcome. There was limited evidence of gender variation in these patterns. Overall, results strengthen the view that children are distinctive members of older adults’ core networks. Further, the role of adult children shifts most noticeably at advanced stages of the disablement process.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"9 1","pages":"194 - 212"},"PeriodicalIF":1.7,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.48","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44077133","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 This paper investigates the distributions of triangle counts per vertex and edge, as a means for network description, analysis, model building, and other tasks. The main interest is in estimating these distributions through sampling, especially for large networks. A novel sampling method tailored for the estimation analysis is proposed, with three sampling designs motivated by several network access scenarios. An estimation method based on inversion and an asymptotic method are developed to recover the entire distribution. A single method to estimate the distribution using multiple samples is also considered. Algorithms are presented to sample the network under the various access scenarios. Finally, the estimation methods on synthetic and real-world networks are evaluated in a data study.
{"title":"Sampling methods and estimation of triangle count distributions in large networks","authors":"Nelson Antunes, Tianjian Guo, V. Pipiras","doi":"10.1017/nws.2021.2","DOIUrl":"https://doi.org/10.1017/nws.2021.2","url":null,"abstract":"Abstract This paper investigates the distributions of triangle counts per vertex and edge, as a means for network description, analysis, model building, and other tasks. The main interest is in estimating these distributions through sampling, especially for large networks. A novel sampling method tailored for the estimation analysis is proposed, with three sampling designs motivated by several network access scenarios. An estimation method based on inversion and an asymptotic method are developed to recover the entire distribution. A single method to estimate the distribution using multiple samples is also considered. Algorithms are presented to sample the network under the various access scenarios. Finally, the estimation methods on synthetic and real-world networks are evaluated in a data study.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"9 1","pages":"S134 - S156"},"PeriodicalIF":1.7,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2021.2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44995788","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 Collaborative production networks among unequal actors manuel muñoz-herrera, jacob dijkstra, andreas flache and rafael wittek 1 Social network change after moving into permanent supportive housing: Who stays and who goes? harmony rhoades, hsun-ta hsu, eric rice, taylor harris, wichada la motte-kerr, hailey winetrobe, benjamin henwood and suzanne wenzel 18 Social cohesion emerging from a community-based physical activity program: A temporal network analysis ana maría jaramillo, felipe montes, olga l. sarmiento, ana paola ríos, lisa g. rosas, ruth f. hunter, ana lucía rodríguez and abby c. king 35 Superbubbles as an empirical characteristic of directed networks fabian gärtner, felix kühnl, carsten r. seemann, the students of the graphs and networks computer lab 2018/19, christian höner zu siederdissen and peter f. stadler 49 Single-seed cascades on clustered networks john k. mcsweeney 59 Sensitivity analysis for network observations with applications to inferences of social influence effects ran xu and kenneth a. frank 73 Analysis of population functional connectivity data via multilayer network embeddings james d.wilson, melanie baybay, rishi sankar, paul stillman and abbie m. popa 99 Imitation, network size, and efficiency carlos alós-ferrer, johannes buckenmaier and federica farolfi 123 network science editorial team
{"title":"NWS volume 9 issue 1 Cover and Back matter","authors":"manuel muñoz-herrera, rafael wittek","doi":"10.1017/nws.2020.47","DOIUrl":"https://doi.org/10.1017/nws.2020.47","url":null,"abstract":"original Articles Collaborative production networks among unequal actors manuel muñoz-herrera, jacob dijkstra, andreas flache and rafael wittek 1 Social network change after moving into permanent supportive housing: Who stays and who goes? harmony rhoades, hsun-ta hsu, eric rice, taylor harris, wichada la motte-kerr, hailey winetrobe, benjamin henwood and suzanne wenzel 18 Social cohesion emerging from a community-based physical activity program: A temporal network analysis ana maría jaramillo, felipe montes, olga l. sarmiento, ana paola ríos, lisa g. rosas, ruth f. hunter, ana lucía rodríguez and abby c. king 35 Superbubbles as an empirical characteristic of directed networks fabian gärtner, felix kühnl, carsten r. seemann, the students of the graphs and networks computer lab 2018/19, christian höner zu siederdissen and peter f. stadler 49 Single-seed cascades on clustered networks john k. mcsweeney 59 Sensitivity analysis for network observations with applications to inferences of social influence effects ran xu and kenneth a. frank 73 Analysis of population functional connectivity data via multilayer network embeddings james d.wilson, melanie baybay, rishi sankar, paul stillman and abbie m. popa 99 Imitation, network size, and efficiency carlos alós-ferrer, johannes buckenmaier and federica farolfi 123 network science editorial team","PeriodicalId":51827,"journal":{"name":"Network Science","volume":" ","pages":"b1 - b2"},"PeriodicalIF":1.7,"publicationDate":"2021-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.47","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48184434","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 State preferences play an important role in international politics. Unfortunately, actually observing and measuring these preferences are impossible. In general, scholars have tried to infer preferences using either UN voting or alliance behavior. The two most notable measures of state preferences that have flowed from this research area are ideal points (Bailey et al., 2017) and S-scores (Signorino & Ritter, 1999). The basis of both these models is a spatial weighting scheme that has proven useful but discounts higher-order effects that might be present in relational data structures such as UN voting and alliances. We begin by arguing that both alliances and UN voting are simply examples of the multiple layers upon which states interact with one another. To estimate a measure of state preferences, we utilize a tensor decomposition model that provides a reduced-rank approximation of the main patterns across the layers. Our new measure of preferences plausibly describes important state relations and yields important insights on the relationship between preferences, democracy, and international conflict. Additionally, we show that a model of conflict using this measure of state preferences decisively outperforms models using extant measures when it comes to predicting conflict in an out-of-sample context.
摘要国家偏好在国际政治中发挥着重要作用。不幸的是，实际观察和测量这些偏好是不可能的。一般来说，学者们试图通过联合国投票或联盟行为来推断偏好。这一研究领域产生的两个最显著的州偏好衡量标准是理想分数（Bailey et al.，2017）和S分数（Signorino&Ritter，1999）。这两个模型的基础都是一个空间加权方案，该方案已被证明是有用的，但不考虑联合国投票和联盟等关系数据结构中可能存在的高阶效应。我们首先认为，联盟和联合国投票只是国家相互作用的多个层面的例子。为了估计状态偏好的度量，我们使用张量分解模型，该模型提供了跨层的主要模式的降阶近似。我们对偏好的新衡量似乎合理地描述了重要的国家关系，并对偏好、民主和国际冲突之间的关系产生了重要的见解。此外，我们还表明，在样本外环境中预测冲突时，使用这种国家偏好衡量标准的冲突模型明显优于使用现有衡量标准的模型。
{"title":"A network approach to measuring state preferences","authors":"Max Gallop, Shahryar Minhas","doi":"10.1017/nws.2020.44","DOIUrl":"https://doi.org/10.1017/nws.2020.44","url":null,"abstract":"Abstract State preferences play an important role in international politics. Unfortunately, actually observing and measuring these preferences are impossible. In general, scholars have tried to infer preferences using either UN voting or alliance behavior. The two most notable measures of state preferences that have flowed from this research area are ideal points (Bailey et al., 2017) and S-scores (Signorino & Ritter, 1999). The basis of both these models is a spatial weighting scheme that has proven useful but discounts higher-order effects that might be present in relational data structures such as UN voting and alliances. We begin by arguing that both alliances and UN voting are simply examples of the multiple layers upon which states interact with one another. To estimate a measure of state preferences, we utilize a tensor decomposition model that provides a reduced-rank approximation of the main patterns across the layers. Our new measure of preferences plausibly describes important state relations and yields important insights on the relationship between preferences, democracy, and international conflict. Additionally, we show that a model of conflict using this measure of state preferences decisively outperforms models using extant measures when it comes to predicting conflict in an out-of-sample context.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"9 1","pages":"135 - 152"},"PeriodicalIF":1.7,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.44","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48686916","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 This study considers a network formation model in which each dyad of agents strategically determines the link status. Our model allows the agents to have unobserved group heterogeneity in the propensity of link formation. For the model estimation, we propose a three-step maximum likelihood method, in which the latent group structure is estimated using the binary segmentation algorithm in the second step. As an empirical illustration, we focus on the network data of international visa-free travels. The results indicate the presence of significant strategic complementarity and a certain level of degree heterogeneity in the network formation behavior.
{"title":"A pairwise strategic network formation model with group heterogeneity: With an application to international travel","authors":"Tadao Hoshino","doi":"10.1017/nws.2022.16","DOIUrl":"https://doi.org/10.1017/nws.2022.16","url":null,"abstract":"Abstract This study considers a network formation model in which each dyad of agents strategically determines the link status. Our model allows the agents to have unobserved group heterogeneity in the propensity of link formation. For the model estimation, we propose a three-step maximum likelihood method, in which the latent group structure is estimated using the binary segmentation algorithm in the second step. As an empirical illustration, we focus on the network data of international visa-free travels. The results indicate the presence of significant strategic complementarity and a certain level of degree heterogeneity in the network formation behavior.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"10 1","pages":"170 - 189"},"PeriodicalIF":1.7,"publicationDate":"2020-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47906994","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 A number of theoretical results have provided sufficient conditions for the selection of payoff-efficient equilibria in games played on networks when agents imitate successful neighbors and make occasional mistakes (stochastic stability). However, those results only guarantee full convergence in the long-run, which might be too restrictive in reality. Here, we employ a more gradual approach relying on agent-based simulations avoiding the double limit underlying these analytical results. We focus on the circular-city model, for which a sufficient condition on the population size relative to the neighborhood size was identified by Alós-Ferrer & Weidenholzer [(2006) Economics Letters, 93, 163–168]. Using more than 100,000 agent-based simulations, we find that selection of the efficient equilibrium prevails also for a large set of parameters violating the previously identified condition. Interestingly, the extent to which efficiency obtains decreases gradually as one moves away from the boundary of this condition.
{"title":"Imitation, network size, and efficiency","authors":"Carlos Alós-Ferrer, J. Buckenmaier, F. Farolfi","doi":"10.1017/nws.2020.43","DOIUrl":"https://doi.org/10.1017/nws.2020.43","url":null,"abstract":"Abstract A number of theoretical results have provided sufficient conditions for the selection of payoff-efficient equilibria in games played on networks when agents imitate successful neighbors and make occasional mistakes (stochastic stability). However, those results only guarantee full convergence in the long-run, which might be too restrictive in reality. Here, we employ a more gradual approach relying on agent-based simulations avoiding the double limit underlying these analytical results. We focus on the circular-city model, for which a sufficient condition on the population size relative to the neighborhood size was identified by Alós-Ferrer & Weidenholzer [(2006) Economics Letters, 93, 163–168]. Using more than 100,000 agent-based simulations, we find that selection of the efficient equilibrium prevails also for a large set of parameters violating the previously identified condition. Interestingly, the extent to which efficiency obtains decreases gradually as one moves away from the boundary of this condition.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"9 1","pages":"123 - 133"},"PeriodicalIF":1.7,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.43","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48332943","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 : 2020-12-01Epub Date: 2020-07-09DOI: 10.1017/nws.2020.24
Ravi Goyal Mathematica, Victor De Gruttola
We present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a broad class of approaches to model trends, seasonal variability, uncertainty, and changes in population composition. Current methods do not account for the variability in the observed historical networks when predicting the network structure; the proposed method provides a principled approach to incorporate uncertainty in prediction. This advance aids in the designing of network-based interventions, as development of such interventions often requires prediction of the network structure in the presence and absence of the intervention. Two simulation studies are conducted to demonstrate the usefulness of generating predicted networks when designing network-based interventions. The framework is also illustrated by investigating results of potential interventions on bill passage rates using a dynamic network that represents the sponsor/co-sponsor relationships among senators derived from bills introduced in the US Senate from 2003-2016.
{"title":"Dynamic Network Prediction.","authors":"Ravi Goyal Mathematica, Victor De Gruttola","doi":"10.1017/nws.2020.24","DOIUrl":"https://doi.org/10.1017/nws.2020.24","url":null,"abstract":"<p><p>We present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a broad class of approaches to model trends, seasonal variability, uncertainty, and changes in population composition. Current methods do not account for the variability in the observed historical networks when predicting the network structure; the proposed method provides a principled approach to incorporate uncertainty in prediction. This advance aids in the designing of network-based interventions, as development of such interventions often requires prediction of the network structure in the presence and absence of the intervention. Two simulation studies are conducted to demonstrate the usefulness of generating predicted networks when designing network-based interventions. The framework is also illustrated by investigating results of potential interventions on bill passage rates using a dynamic network that represents the sponsor/co-sponsor relationships among senators derived from bills introduced in the US Senate from 2003-2016.</p>","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"8 4","pages":"574-595"},"PeriodicalIF":1.7,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.24","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33444817","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}
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