{"title":"A Sequential and Scalable Approach to Community Detection in Dynamic Graphs","authors":"Andre Beckus, George K. Atia","doi":"10.1109/ALLERTON.2019.8919954","DOIUrl":null,"url":null,"abstract":"We study a sequential sketch-based approach for the clustering of time-evolving graphs. We present a dynamic extension to the static Stochastic Block Model, which accommo- dates growing and shrinking graphs, as well as the movement of nodes between clusters. We then propose an online algorithm which constructs and maintains a small sketch consisting of nodes sampled from the full graph. The sketch is clustered and a retrieval algorithm is used to infer cluster membership of nodes in each successive graph snapshot. We demonstrate that the use of a small sketch not only improves computational complexity, but also improves the success rate when sketches are properly proportioned. We present a sampling method which chooses nodes according to node degree, whereby very small clusters can be successfully tracked.","PeriodicalId":120479,"journal":{"name":"2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ALLERTON.2019.8919954","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
We study a sequential sketch-based approach for the clustering of time-evolving graphs. We present a dynamic extension to the static Stochastic Block Model, which accommo- dates growing and shrinking graphs, as well as the movement of nodes between clusters. We then propose an online algorithm which constructs and maintains a small sketch consisting of nodes sampled from the full graph. The sketch is clustered and a retrieval algorithm is used to infer cluster membership of nodes in each successive graph snapshot. We demonstrate that the use of a small sketch not only improves computational complexity, but also improves the success rate when sketches are properly proportioned. We present a sampling method which chooses nodes according to node degree, whereby very small clusters can be successfully tracked.