Pub Date : 2024-01-01Epub Date: 2024-10-03DOI: 10.1007/s41109-024-00670-y
Xin Ran, Ellen Meara, Nancy E Morden, Erika L Moen, Daniel N Rockmore, A James O'Malley
<p><p>Social network analysis and shared-patient physician networks have become effective ways of studying physician collaborations. Assortative mixing or "homophily" is the network phenomenon whereby the propensity for similar individuals to form ties is greater than for dissimilar individuals. Motivated by the public health concern of risky-prescribing among older patients in the United States, we develop network models and tests involving novel network measures to study whether there is evidence of homophily in prescribing and deprescribing in the specific shared-patient network of physicians linked to the US state of Ohio in 2014. Evidence of homophily in risky-prescribing would imply that prescribing behaviors help shape physician networks and would suggest strategies for interventions seeking to reduce risky-prescribing (e.g., strategies to directly reduce risky prescribing might be most effective if applied as group interventions to risky prescribing physicians connected through the network and the connections between these physicians could be targeted by tie dissolution interventions as an indirect way of reducing risky prescribing). Furthermore, if such effects varied depending on the structural features of a physician's position in the network (e.g., by whether or not they are involved in cliques-groups of actors that are fully connected to each other-such as closed triangles in the case of three actors), this would further strengthen the case for targeting groups of physicians involved in risky prescribing and the network connections between them for interventions. Using accompanying Medicare Part D data, we converted patient longitudinal prescription receipts into novel measures of the intensity of each physician's risky-prescribing. Exponential random graph models were used to simultaneously estimate the importance of homophily in prescribing and deprescribing in the network beyond the characteristics of physician specialty (or other metadata) and network-derived features. In addition, novel network measures were introduced to allow homophily to be characterized in relation to specific triadic (three-actor) structural configurations in the network with associated non-parametric randomization tests to evaluate their statistical significance in the network against the null hypothesis of no such phenomena. We found physician homophily in prescribing and deprescribing. We also found that physicians exhibited within-triad homophily in risky-prescribing, with the prevalence of homophilic triads significantly higher than expected by chance absent homophily. These results may explain why communities of prescribers emerge and evolve, helping to justify group-level prescriber interventions. The methodology may be applied, adapted or generalized to study homophily and its generalizations on other network and attribute combinations involving analogous shared-patient networks and more generally using other kinds of network data underlying other k
社会网络分析和共享病人的医生网络已成为研究医生合作的有效方法。同类混合（Assortative Mixing）或 "同质性"（homophily）是一种网络现象，即相似个体形成联系的倾向大于不同个体。出于对美国老年患者开具风险处方这一公共卫生问题的关注，我们建立了网络模型，并使用新型网络测量方法进行测试，以研究在 2014 年与美国俄亥俄州相关联的特定医生共享患者网络中，是否存在开具处方和取消处方的同质性证据。风险处方的同质性证据将意味着处方行为有助于形成医生网络，并将为寻求减少风险处方的干预措施提出建议（例如，如果将直接减少风险处方的策略作为群体干预措施应用于通过网络连接的风险处方医生，则可能最为有效，而这些医生之间的联系可以作为减少风险处方的一种间接方式，通过纽带解体干预措施加以解决）。此外，如果这种效果因医生在网络中的位置结构特征而异（例如，根据他们是否参与小团体--彼此完全连接的行为者群体--如三个行为者的封闭三角形），这将进一步加强针对参与风险处方的医生群体以及他们之间的网络连接进行干预的理由。利用随附的医疗保险 D 部分数据，我们将患者的纵向处方收据转换为衡量每位医生风险处方强度的新指标。我们使用指数随机图模型同时估算了医生专业特征（或其他元数据）和网络衍生特征之外，网络中开具处方和取消处方的同质性的重要性。此外，我们还引入了新的网络度量方法，以便根据网络中特定的三元（三因素）结构配置来描述同质性，并进行相关的非参数随机检验，以评估其在网络中的统计意义，并与无此类现象的零假设进行对比。我们发现医生在开处方和取消处方方面具有同质性。我们还发现，医生在开具风险处方时表现出了同族三人组，同族三人组的发生率明显高于不存在同族三人组的偶然性。这些结果可以解释开处方者群体出现和发展的原因，有助于证明群体层面的开处方者干预措施的合理性。该方法可以应用、调整或推广，以研究同质性及其在其他网络和属性组合（涉及类似的共享患者网络）上的普遍性，并更广泛地使用其他类型的网络数据来揭示其他类型的社会现象。
{"title":"Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships.","authors":"Xin Ran, Ellen Meara, Nancy E Morden, Erika L Moen, Daniel N Rockmore, A James O'Malley","doi":"10.1007/s41109-024-00670-y","DOIUrl":"10.1007/s41109-024-00670-y","url":null,"abstract":"<p><p>Social network analysis and shared-patient physician networks have become effective ways of studying physician collaborations. Assortative mixing or \"homophily\" is the network phenomenon whereby the propensity for similar individuals to form ties is greater than for dissimilar individuals. Motivated by the public health concern of risky-prescribing among older patients in the United States, we develop network models and tests involving novel network measures to study whether there is evidence of homophily in prescribing and deprescribing in the specific shared-patient network of physicians linked to the US state of Ohio in 2014. Evidence of homophily in risky-prescribing would imply that prescribing behaviors help shape physician networks and would suggest strategies for interventions seeking to reduce risky-prescribing (e.g., strategies to directly reduce risky prescribing might be most effective if applied as group interventions to risky prescribing physicians connected through the network and the connections between these physicians could be targeted by tie dissolution interventions as an indirect way of reducing risky prescribing). Furthermore, if such effects varied depending on the structural features of a physician's position in the network (e.g., by whether or not they are involved in cliques-groups of actors that are fully connected to each other-such as closed triangles in the case of three actors), this would further strengthen the case for targeting groups of physicians involved in risky prescribing and the network connections between them for interventions. Using accompanying Medicare Part D data, we converted patient longitudinal prescription receipts into novel measures of the intensity of each physician's risky-prescribing. Exponential random graph models were used to simultaneously estimate the importance of homophily in prescribing and deprescribing in the network beyond the characteristics of physician specialty (or other metadata) and network-derived features. In addition, novel network measures were introduced to allow homophily to be characterized in relation to specific triadic (three-actor) structural configurations in the network with associated non-parametric randomization tests to evaluate their statistical significance in the network against the null hypothesis of no such phenomena. We found physician homophily in prescribing and deprescribing. We also found that physicians exhibited within-triad homophily in risky-prescribing, with the prevalence of homophilic triads significantly higher than expected by chance absent homophily. These results may explain why communities of prescribers emerge and evolve, helping to justify group-level prescriber interventions. The methodology may be applied, adapted or generalized to study homophily and its generalizations on other network and attribute combinations involving analogous shared-patient networks and more generally using other kinds of network data underlying other k","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142381887","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}
Pub Date : 2024-01-01Epub Date: 2024-04-30DOI: 10.1007/s41109-024-00616-4
Octavious Smiley, Till Hoffmann, Jukka-Pekka Onnela
Network models are increasingly used to study infectious disease spread. Exponential Random Graph models have a history in this area, with scalable inference methods now available. An alternative approach uses mechanistic network models. Mechanistic network models directly capture individual behaviors, making them suitable for studying sexually transmitted diseases. Combining mechanistic models with Approximate Bayesian Computation allows flexible modeling using domain-specific interaction rules among agents, avoiding network model oversimplifications. These models are ideal for longitudinal settings as they explicitly incorporate network evolution over time. We implemented a discrete-time version of a previously published continuous-time model of evolving contact networks for men who have sex with men and proposed an ABC-based approximate inference scheme for it. As expected, we found that a two-wave longitudinal study design improves the accuracy of inference compared to a cross-sectional design. However, the gains in precision in collecting data twice, up to 18%, depend on the spacing of the two waves and are sensitive to the choice of summary statistics. In addition to methodological developments, our results inform the design of future longitudinal network studies in sexually transmitted diseases, specifically in terms of what data to collect from participants and when to do so.
{"title":"Approximate inference for longitudinal mechanistic HIV contact network.","authors":"Octavious Smiley, Till Hoffmann, Jukka-Pekka Onnela","doi":"10.1007/s41109-024-00616-4","DOIUrl":"https://doi.org/10.1007/s41109-024-00616-4","url":null,"abstract":"<p><p>Network models are increasingly used to study infectious disease spread. Exponential Random Graph models have a history in this area, with scalable inference methods now available. An alternative approach uses mechanistic network models. Mechanistic network models directly capture individual behaviors, making them suitable for studying sexually transmitted diseases. Combining mechanistic models with Approximate Bayesian Computation allows flexible modeling using domain-specific interaction rules among agents, avoiding network model oversimplifications. These models are ideal for longitudinal settings as they explicitly incorporate network evolution over time. We implemented a discrete-time version of a previously published continuous-time model of evolving contact networks for men who have sex with men and proposed an ABC-based approximate inference scheme for it. As expected, we found that a two-wave longitudinal study design improves the accuracy of inference compared to a cross-sectional design. However, the gains in precision in collecting data twice, up to 18%, depend on the spacing of the two waves and are sensitive to the choice of summary statistics. In addition to methodological developments, our results inform the design of future longitudinal network studies in sexually transmitted diseases, specifically in terms of what data to collect from participants and when to do so.</p>","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11060975/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140870121","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}
Pub Date : 2023-12-01DOI: 10.1007/s41109-023-00606-y
Paulo Eduardo Althoff, Alan Demétrius Baria Valejo, Thiago de Paulo Faleiros
{"title":"Coarsening effects on k-partite network classification","authors":"Paulo Eduardo Althoff, Alan Demétrius Baria Valejo, Thiago de Paulo Faleiros","doi":"10.1007/s41109-023-00606-y","DOIUrl":"https://doi.org/10.1007/s41109-023-00606-y","url":null,"abstract":"","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138621630","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 : 2023-11-30DOI: 10.1007/s41109-023-00605-z
Yoosof Mashayekhi, Alireza Rezvanian, S. M. Vahidipour
{"title":"A novel regularized weighted estimation method for information diffusion prediction in social networks","authors":"Yoosof Mashayekhi, Alireza Rezvanian, S. M. Vahidipour","doi":"10.1007/s41109-023-00605-z","DOIUrl":"https://doi.org/10.1007/s41109-023-00605-z","url":null,"abstract":"","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139198137","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 : 2023-11-13DOI: 10.1007/s41109-023-00604-0
Kashin Sugishita, Naoki Masuda
Abstract Manga, Japanese comics, has been popular on a global scale. Social networks among characters, which are often called character networks, may be a significant contributor to their popularity. We collected data from 162 popular manga that span over 70 years and analyzed their character networks. First, we found that many of static and temporal properties of the character networks are similar to those of real human social networks. Second, the character networks of most manga are protagonist-centered such that a single protagonist interacts with the majority of other characters. Third, the character networks for manga mainly targeting boys have shifted to denser and less protagonist-centered networks and with fewer characters over decades. Manga mainly targeting girls showed the opposite trend except for the downward trend in the number of characters. The present study, which relies on manga data sampled on an unprecedented scale, paves the way for further population studies of character networks and other aspects of comics.
{"title":"Social network analysis of manga: similarities to real-world social networks and trends over decades","authors":"Kashin Sugishita, Naoki Masuda","doi":"10.1007/s41109-023-00604-0","DOIUrl":"https://doi.org/10.1007/s41109-023-00604-0","url":null,"abstract":"Abstract Manga, Japanese comics, has been popular on a global scale. Social networks among characters, which are often called character networks, may be a significant contributor to their popularity. We collected data from 162 popular manga that span over 70 years and analyzed their character networks. First, we found that many of static and temporal properties of the character networks are similar to those of real human social networks. Second, the character networks of most manga are protagonist-centered such that a single protagonist interacts with the majority of other characters. Third, the character networks for manga mainly targeting boys have shifted to denser and less protagonist-centered networks and with fewer characters over decades. Manga mainly targeting girls showed the opposite trend except for the downward trend in the number of characters. The present study, which relies on manga data sampled on an unprecedented scale, paves the way for further population studies of character networks and other aspects of comics.","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136282281","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 : 2023-11-09DOI: 10.1007/s41109-023-00601-3
Nicholas Rabb, Lenore Cowen, Jan P. de Ruiter
Abstract The degree of polarization in many societies has become a pressing concern in media studies. Typically, it is argued that the internet and social media have created more media producers than ever before, allowing individual, biased media consumers to expose themselves only to what already confirms their beliefs, leading to polarized echo-chambers that further deepen polarization. This work introduces extensions to the recent Cognitive Cascades model of Rabb et al. to study this dynamic, allowing for simulation of information spread between media and networks of variably biased citizens. Our results partially confirm the above polarization logic, but also reveal several important enabling conditions for polarization to occur: (1) the distribution of media belief must be more polarized than the population; (2) the population must be at least somewhat persuadable to changing their belief according to new messages they hear; and finally, (3) the media must statically continue to broadcast more polarized messages rather than, say, adjust to appeal more to the beliefs of their current subscribers. Moreover, and somewhat counter-intuitively, under these conditions we find that polarization is more likely to occur when media consumers are exposed to more diverse messages, and that polarization occurred most often when there were low levels of echo-chambers and fragmentation. These results suggest that polarization is not simply due to biased individuals responding to an influx of media sources in the digital age, but also a consequence of polarized media conditions within an information ecosystem that supports more diverse exposure than is typically thought.
{"title":"Investigating the effect of selective exposure, audience fragmentation, and echo-chambers on polarization in dynamic media ecosystems","authors":"Nicholas Rabb, Lenore Cowen, Jan P. de Ruiter","doi":"10.1007/s41109-023-00601-3","DOIUrl":"https://doi.org/10.1007/s41109-023-00601-3","url":null,"abstract":"Abstract The degree of polarization in many societies has become a pressing concern in media studies. Typically, it is argued that the internet and social media have created more media producers than ever before, allowing individual, biased media consumers to expose themselves only to what already confirms their beliefs, leading to polarized echo-chambers that further deepen polarization. This work introduces extensions to the recent Cognitive Cascades model of Rabb et al. to study this dynamic, allowing for simulation of information spread between media and networks of variably biased citizens. Our results partially confirm the above polarization logic, but also reveal several important enabling conditions for polarization to occur: (1) the distribution of media belief must be more polarized than the population; (2) the population must be at least somewhat persuadable to changing their belief according to new messages they hear; and finally, (3) the media must statically continue to broadcast more polarized messages rather than, say, adjust to appeal more to the beliefs of their current subscribers. Moreover, and somewhat counter-intuitively, under these conditions we find that polarization is more likely to occur when media consumers are exposed to more diverse messages, and that polarization occurred most often when there were low levels of echo-chambers and fragmentation. These results suggest that polarization is not simply due to biased individuals responding to an influx of media sources in the digital age, but also a consequence of polarized media conditions within an information ecosystem that supports more diverse exposure than is typically thought.","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135291145","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 : 2023-11-09DOI: 10.1007/s41109-023-00603-1
Gustavo Pilatti, Cristian Candia, Alessandra Montini, Flávio L. Pinheiro
Abstract The labor market has transformed with the advent of the gig economy, characterized by short-term and flexible work arrangements facilitated by online platforms. As this trend becomes increasingly prevalent, it presents unique opportunities and challenges. In this manuscript, we comprehensively characterize the social networks of gig economy workers in each of the 15 cities studied. Our analysis reveals a scaling relationship between networks and the city population. In particular, we note the high level of modularity of the networks, and we argue that it results from the natural specialization of couriers along different areas of the cities. Furthermore, we show that degree and betweenness centrality is positively correlated with income but not with tenure. Our findings shed new light on the social organization of the gig economy workers and provide valuable insights for the management and design of gig economy platforms.
{"title":"From co-location patterns to an informal social network of gig economy workers","authors":"Gustavo Pilatti, Cristian Candia, Alessandra Montini, Flávio L. Pinheiro","doi":"10.1007/s41109-023-00603-1","DOIUrl":"https://doi.org/10.1007/s41109-023-00603-1","url":null,"abstract":"Abstract The labor market has transformed with the advent of the gig economy, characterized by short-term and flexible work arrangements facilitated by online platforms. As this trend becomes increasingly prevalent, it presents unique opportunities and challenges. In this manuscript, we comprehensively characterize the social networks of gig economy workers in each of the 15 cities studied. Our analysis reveals a scaling relationship between networks and the city population. In particular, we note the high level of modularity of the networks, and we argue that it results from the natural specialization of couriers along different areas of the cities. Furthermore, we show that degree and betweenness centrality is positively correlated with income but not with tenure. Our findings shed new light on the social organization of the gig economy workers and provide valuable insights for the management and design of gig economy platforms.","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135243034","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 : 2023-11-07DOI: 10.1007/s41109-023-00587-y
Kai Bergermann, Margitta Wolter
Abstract Ten years after the collapse of the Rana Plaza textile factory in Dhaka, Bangladesh that killed over 1000 factory workers, the event has become a symbol for the desolate working conditions in fast fashion producer countries in the global south. We analyze the global Twitter discourse on this event over a three week window around the collapse date over the years 2013–2022 by a mixture of network-theoretic quantitative and discourse-theoretic qualitative methods. In particular, key communicators and the community structure of the discourse participants are identified using a multilayer network modeling approach and the interpretative patterns of the key communicator’s tweets of all years are analyzed using the sociology of knowledge approach to discourse. This combination of quantitative and qualitative methods reveals that the discourse is separated into three phases: reporting, reprocessing, and commemoration. These phases can be identified by the temporal evolution, network-structural properties, and the contentual analysis of the discourse. After the negotiation of the interpretative framework in the reprocessing phase, subsequent years are characterized by its commemorative repetition as well as resulting demands by different international actor groups despite highly fluctuating participants.
{"title":"A Twitter network and discourse analysis of the Rana Plaza collapse","authors":"Kai Bergermann, Margitta Wolter","doi":"10.1007/s41109-023-00587-y","DOIUrl":"https://doi.org/10.1007/s41109-023-00587-y","url":null,"abstract":"Abstract Ten years after the collapse of the Rana Plaza textile factory in Dhaka, Bangladesh that killed over 1000 factory workers, the event has become a symbol for the desolate working conditions in fast fashion producer countries in the global south. We analyze the global Twitter discourse on this event over a three week window around the collapse date over the years 2013–2022 by a mixture of network-theoretic quantitative and discourse-theoretic qualitative methods. In particular, key communicators and the community structure of the discourse participants are identified using a multilayer network modeling approach and the interpretative patterns of the key communicator’s tweets of all years are analyzed using the sociology of knowledge approach to discourse. This combination of quantitative and qualitative methods reveals that the discourse is separated into three phases: reporting, reprocessing, and commemoration. These phases can be identified by the temporal evolution, network-structural properties, and the contentual analysis of the discourse. After the negotiation of the interpretative framework in the reprocessing phase, subsequent years are characterized by its commemorative repetition as well as resulting demands by different international actor groups despite highly fluctuating participants.","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135480546","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 : 2023-11-07DOI: 10.1007/s41109-023-00598-9
Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset, Youngser Park, Carey E. Priebe
Abstract Random graphs are increasingly becoming objects of interest for modeling networks in a wide range of applications. Latent position random graph models posit that each node is associated with a latent position vector, and that these vectors follow some geometric structure in the latent space. In this paper, we consider random dot product graphs, in which an edge is formed between two nodes with probability given by the inner product of their respective latent positions. We assume that the latent position vectors lie on an unknown one-dimensional curve and are coupled with a response covariate via a regression model. Using the geometry of the underlying latent position vectors, we propose a manifold learning and graph embedding technique to predict the response variable on out-of-sample nodes, and we establish convergence guarantees for these responses. Our theoretical results are supported by simulations and an application to Drosophila brain data.
{"title":"Semisupervised regression in latent structure networks on unknown manifolds","authors":"Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset, Youngser Park, Carey E. Priebe","doi":"10.1007/s41109-023-00598-9","DOIUrl":"https://doi.org/10.1007/s41109-023-00598-9","url":null,"abstract":"Abstract Random graphs are increasingly becoming objects of interest for modeling networks in a wide range of applications. Latent position random graph models posit that each node is associated with a latent position vector, and that these vectors follow some geometric structure in the latent space. In this paper, we consider random dot product graphs, in which an edge is formed between two nodes with probability given by the inner product of their respective latent positions. We assume that the latent position vectors lie on an unknown one-dimensional curve and are coupled with a response covariate via a regression model. Using the geometry of the underlying latent position vectors, we propose a manifold learning and graph embedding technique to predict the response variable on out-of-sample nodes, and we establish convergence guarantees for these responses. Our theoretical results are supported by simulations and an application to Drosophila brain data.","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135480184","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 : 2023-11-07DOI: 10.1007/s41109-023-00597-w
Li Zou, Alberto Ceria, Huijuan Wang
Abstract Temporal networks are networks whose topology changes over time. Two nodes in a temporal network are connected at a discrete time step only if they have a contact/interaction at that time. The classic temporal network prediction problem aims to predict the temporal network one time step ahead based on the network observed in the past of a given duration. This problem has been addressed mostly via machine learning algorithms, at the expense of high computational costs and limited interpretation of the underlying mechanisms that form the networks. Hence, we propose to predict the connection of each node pair one step ahead based on the connections of this node pair itself and of node pairs that share a common node with this target node pair in the past. The concrete design of our two prediction models is based on the analysis of the memory property of real-world physical networks, i.e., to what extent two snapshots of a network at different times are similar in topology (or overlap). State-of-the-art prediction methods that allow interpretation are considered as baseline models. In seven real-world physical contact networks, our methods are shown to outperform the baselines in both prediction accuracy and computational complexity. They perform better in networks with stronger memory. Importantly, our models reveal how the connections of different types of node pairs in the past contribute to the connection estimation of a target node pair. Predicting temporal networks like physical contact networks in the long-term future beyond short-term i.e., one step ahead is crucial to forecast and mitigate the spread of epidemics and misinformation on the network. This long-term prediction problem has been seldom explored. Therefore, we propose basic methods that adapt each aforementioned prediction model to address classic short-term network prediction problem for long-term network prediction task. The prediction quality of all adapted models is evaluated via the accuracy in predicting each network snapshot and in reproducing key network properties. The prediction based on one of our models tends to have the highest accuracy and lowest computational complexity.
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