Applied Network Science最新文献

{"title":"Navigation on temporal networks.","authors":"Omar F Robledo, Petter Holme, Huijuan Wang","doi":"10.1007/s41109-025-00697-9","DOIUrl":"10.1007/s41109-025-00697-9","url":null,"abstract":"<p><p>Temporal networks, whose network topology changes over time, are used to represent, e.g., opportunistic mobile networks, vehicle networks, and social contact networks, where two mobile devices (autos or individuals) are connected only when they are close to (interact with) each other. Such networks facilitate the transfer of information. In this paper, we address the problem of navigation on temporal networks: how to route a traffic demand from a source <i>s</i> to a destination <i>d</i> at time <math><msub><mi>t</mi> <mi>s</mi></msub> </math> , based on the network observed before <math><msub><mi>t</mi> <mi>s</mi></msub> </math> ? Whenever the node hosting the information has a contact or interacts with another node, the routing method has to decide whether the information should be forwarded to the contacted node or not. Once the information is forwarded, the contacted node becomes the only node hosting the information. Firstly, we introduce a framework of designing navigation algorithms, in which a distance metric is defined and computed between any node to the target <i>d</i> based on the network observed before <math><msub><mi>t</mi> <mi>s</mi></msub> </math> . Whenever a hosting node has a contact, it forwards the information to the contacted node if the contacted node is closer to the target than the hosting node according to the distance metric. Secondly, we propose systematically distance metrics of a node pair in the temporal network observed, that capture different network properties of a node pair. Thirdly, these metrics or routing strategies are evaluated in empirical contact networks, from the perspective of the time duration of the routing and the probability that the destination can be reached. Their performance is further explained via the correlation between distance metrics and the stability of each metric in ranking nodes' distance to a target node. This work may serve as inspiration for evaluating and redesigning these strategies in other types of networks beyond physical contact networks.</p>","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":"10 1","pages":"7"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926000/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143693827","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}

{"title":"Influence of multiple network structures on bayesian estimation of peer effects and statistical power for generalized linear network autocorrelation models.","authors":"Guanqing Chen, A James O'Malley","doi":"10.1007/s41109-025-00709-8","DOIUrl":"10.1007/s41109-025-00709-8","url":null,"abstract":"<p><p>The recent published literature on linear network autocorrelation models of actor behaviors or other mutable attributes has revealed a curious finding. Irrespective of the size of the network and the status of other network features, likelihood-based estimators (e.g., maximum likelihood and Bayesian) of the autocorrelation parameter ([Formula: see text]) are negatively biased and become increasingly so as the density of the network increases. In this paper we investigate the pattern of bias of estimators of [Formula: see text] when analyzing multiple mutually exclusive sub-networks and directed networks with various levels of reciprocity. In addition to considering the case of a linear network autocorrelation model applied to a binary-valued network, the edges may be weighted and the attribute whose actor-interdependence (or peer-effect) we are interested in may be an event (i.e., a binary outcome), a count, or a rate outcome motivating the use of generalized linear network autocorrelation models. We perform a simulation study that reveals that bias reduces substantially as either the number of sub-networks increases or with increased variation across the network in the edge weights but this pattern is not observed with reciprocity. The findings for generalized linear network autocorrelation models are in general similar to those for linear network autocorrelation models. Finally, we perform a statistical power analysis based on these findings for use in designing future studies whose goal is to estimate or to detect peer-effects.</p>","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":"10 1","pages":"18"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12126333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144209808","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}

{"title":"Temporal dynamics of the friendship paradox in a smartphone communication network.","authors":"Cheng Wang, Omar Lizardo, David S Hachen","doi":"10.1007/s41109-025-00710-1","DOIUrl":"10.1007/s41109-025-00710-1","url":null,"abstract":"<p><p>The friendship paradox, initially discussed by Scott Feld in 1991, highlights a counterintuitive social phenomenon where individuals tend to have fewer friends than their friends do on average. The sociological implications of this paradox are profound, as it can create a distorted understanding of social norms and consequently influence beliefs, attitudes, and behaviors, particularly when highly connected individuals present a skewed representation of those norms. In essence, it can lead individuals to misjudge what is typical or desirable within their social circles. This study investigates the temporal dynamics of the friendship paradox using smartphone communication data from over 600 incoming freshmen at the University of Notre Dame participating in the NetHealth project. By tracking the friendship index- the ratio of an individual's friends' average number of friends to their own number of friends- over 119 days during the Fall semester of 2015, we examine how the paradox evolves over time. Our findings reveal that the friendship index stabilizes more rapidly than both the individuals' own degree and the variation among their friends' degrees. Results from the latent growth-curve model (LGCM) confirm that while the friendship index continues to increase, its growth rate declines over time. Moreover, the LGCM identifies individual degrees, ethnic backgrounds, and personality traits as influential factors shaping the manifestation and development of the friendship paradox. By exploring the mechanisms underlying this paradox in a dynamic communication network, this study enhances our understanding of the structural factors influencing the evolution of the friendship paradox in digitally mediated interactions.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s41109-025-00710-1.</p>","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":"10 1","pages":"16"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144143855","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}

{"title":"Accounting for contact network uncertainty in epidemic inferences with Approximate Bayesian Computation.","authors":"Maxwell H Wang, Jukka-Pekka Onnela","doi":"10.1007/s41109-025-00694-y","DOIUrl":"https://doi.org/10.1007/s41109-025-00694-y","url":null,"abstract":"<p><p>In models of infectious disease dynamics, the incorporation of contact network information allows for the capture of the non-randomness and heterogeneity of realistic contact patterns. Oftentimes, it is assumed that this underlying network is known with perfect certainty. However, in realistic settings, the observed data usually serves as an imperfect proxy of the actual contact patterns in the population. Furthermore, event times in observed epidemics are not perfectly recorded; individual infection and recovery times are often missing. In order to conduct accurate inferences on parameters of contagion spread, it is crucial to incorporate these sources of uncertainty. In this paper, we propose the use of Network-augmented Mixture Density Network-compressed ABC (NA-MDN-ABC) to learn informative summary statistics for the available data. This method will allow for Bayesian inference on the parameters of a contagious process, while accounting for imperfect observations on the epidemic and the contact network. We will demonstrate the use of this method on simulated epidemics and networks, and extend this framework to analyze the spread of Tattoo Skin Disease (TSD) among bottlenose dolphins in Shark Bay, Australia.</p>","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":"10 1","pages":"13"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12014783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022870","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}

{"title":"American politics in 3D: measuring multidimensional issue alignment in social media using social graphs and text data","authors":"Pedro Ramaciotti, Duncan Cassells, Zografoula Vagena, Jean-Philippe Cointet, Michael Bailey","doi":"10.1007/s41109-023-00608-w","DOIUrl":"https://doi.org/10.1007/s41109-023-00608-w","url":null,"abstract":"","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":"73 22","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139440546","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}

{"title":"A generalized eigenvector centrality for multilayer networks with inter-layer constraints on adjacent node importance.","authors":"H Robert Frost","doi":"10.1007/s41109-024-00620-8","DOIUrl":"10.1007/s41109-024-00620-8","url":null,"abstract":"<p><p>We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by multiple edge-weighted, potentially directed, graphs over the same set of nodes with each graph representing one layer of the network and no inter-layer edges. As in the standard eigenvector centrality construction, the importance of each node in a given layer is based on the weighted sum of the importance of adjacent nodes in that same layer. Unlike standard eigenvector centrality, we assume that the adjacency relationship and the importance of adjacent nodes may be based on distinct layers. Importantly, this type of centrality constraint is only partially supported by existing frameworks for multilayer eigenvector centrality that use edges between nodes in different layers to capture inter-layer dependencies. For our model, constrained, layer-specific eigenvector centrality values are defined by a system of independent eigenvalue problems and dependent pseudo-eigenvalue problems, whose solution can be efficiently realized using an interleaved power iteration algorithm. We refer to this model, and the associated algorithm, as the Constrained Multilayer Centrality (CMLC) method. The characteristics of this approach, and of standard techniques based on inter-layer edges, are demonstrated on both a simple multilayer network and on a range of random graph models. An R package implementing the CMLC method along with example vignettes is available at https://hrfrost.host.dartmouth.edu/CMLC/.</p>","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":"9 1","pages":"14"},"PeriodicalIF":2.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11060970/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140853977","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}

{"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":"&lt;p&gt;&lt;p&gt;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":"9 1","pages":"63"},"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}

{"title":"Leading by the nodes: a survey of film industry network analysis and datasets.","authors":"Aresh Dadlani, Vi Vo, Ayushi Khemka, Sophie Talalay Harvey, Aigul Kantoro Kyzy, Pete Jones, Deb Verhoeven","doi":"10.1007/s41109-024-00673-9","DOIUrl":"10.1007/s41109-024-00673-9","url":null,"abstract":"<p><p>This paper presents a comprehensive survey of network analysis research on the film industry, aiming to evaluate its emergence as a field of study and identify potential areas for further research. Many foundational network studies made use of the abundant data from the Internet Movie Database (IMDb) to test network methodologies. This survey focuses more specifically on examining research that employs network analysis to evaluate the film industry itself, revealing the social and business relationships involved in film production, distribution, and consumption. The paper adopts a classification approach based on node type and summarises the key contributions in relation to each. The review provides insights into the structure and interconnectedness of the field, highlighting clusters of debates and shedding light on the areas in need of further theoretical and methodological development. In addition, this survey contributes to understanding film industry network analysis and informs researchers interested in network methods within the film industry and related cultural sectors.</p>","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":"9 1","pages":"76"},"PeriodicalIF":1.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142877782","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}

{"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":"9 1","pages":"12"},"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}

{"title":"Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients.","authors":"Guanqing Chen, A James O'Malley","doi":"10.1007/s41109-024-00627-1","DOIUrl":"10.1007/s41109-024-00627-1","url":null,"abstract":"<p><p>When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. In such a situation, a patient's own hospital can be thought of as a mediator between the effects of peer hospitals and their outcome. However, as in mediation analyses, there may be interest in allowing the effects of peer units to directly impact patients of other units. To accommodate these possibilities, we develop two hierarchical network autocorrelation models that allow for direct and indirect peer effects between hospitals when modeling individual outcomes of the patients cared for at the hospitals. A Bayesian approach is used for model estimation while a simulation study assesses the performance of the models and sensitivity of results to different prior distributions. We construct a United States New England region patient-sharing hospital network and apply newly developed Bayesian hierarchical models to study the diffusion of robotic surgery and hospital peer effects in patient outcomes using a cohort of United States Medicare beneficiaries in 2016 and 2017. The comparative fit of models to the data is assessed using Deviance information criteria tailored to hierarchical models that include peer effects as latent variables.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s41109-024-00627-1.</p>","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":"9 1","pages":"24"},"PeriodicalIF":1.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11636997/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819646","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}