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Latent variable models using Bayesian statistical inference are powerful tools to represent such networks. One such latent variable network model is a Mixed Membership Stochastic Blockmodel (MMSB), which can discover overlapping communities in a network and has high predictive power. Previous inference methods estimate the latent variables and unknown parameters of the MMSB on the basis of the whole observed network. Therefore, dynamic changes in network structure over time are hard to track. Thus, we present a particle filter based on node activities with various term lengths for online sequential estimation of the MMSB. For instance, in an e-mail communication network, each particle only considers e-mail accounts that sent or received a message within a specific term length, where the length may be different from those of other particles. We show through experiments that our proposed methods achieve both high prediction performance and computational efficiency."}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1380004236278847744","@type":"Researcher","foaf:name":[{"@value":"Shohei Sakamoto"}],"jpcoar:affiliationName":[{"@value":"Kobe University, Kobe, Japan"}]},{"@id":"https://cir.nii.ac.jp/crid/1420282801209322624","@type":"Researcher","personIdentifier":[{"@type":"KAKEN_RESEARCHERS","@value":"50321576"},{"@type":"NRID","@value":"1000050321576"},{"@type":"NRID","@value":"9000006793426"},{"@type":"NRID","@value":"9000004494267"},{"@type":"NRID","@value":"9000006775370"},{"@type":"NRID","@value":"9000321620796"},{"@type":"NRID","@value":"9000244924281"},{"@type":"NRID","@value":"9000241629123"},{"@type":"NRID","@value":"9000020915930"},{"@type":"NRID","@value":"9000263065996"},{"@type":"NRID","@value":"9000264862615"},{"@type":"NRID","@value":"9000378076895"},{"@type":"NRID","@value":"9000243893543"},{"@type":"NRID","@value":"9000413754492"},{"@type":"NRID","@value":"9000264862149"},{"@type":"NRID","@value":"9000345217940"},{"@type":"NRID","@value":"9000243893521"},{"@type":"NRID","@value":"9000022128935"},{"@type":"NRID","@value":"9000347318553"},{"@type":"NRID","@value":"9000241628974"},{"@type":"NRID","@value":"9000238286046"},{"@type":"NRID","@value":"9000002541481"},{"@type":"NRID","@value":"9000005893538"},{"@type":"NRID","@value":"9000317564919"},{"@type":"NRID","@value":"9000404268441"},{"@type":"NRID","@value":"9000020658298"},{"@type":"NRID","@value":"9000391542836"},{"@type":"NRID","@value":"9000402242134"},{"@type":"NRID","@value":"9000010722229"},{"@type":"NRID","@value":"9000021937536"},{"@type":"NRID","@value":"9000378076893"},{"@type":"NRID","@value":"9000309560286"},{"@type":"NRID","@value":"9000243893556"},{"@type":"NRID","@value":"9000264863009"},{"@type":"NRID","@value":"9000369737187"},{"@type":"NRID","@value":"9000238285941"},{"@type":"NRID","@value":"9000386383799"},{"@type":"NRID","@value":"9000017681862"},{"@type":"NRID","@value":"9000244927274"},{"@type":"NRID","@value":"9000404271797"},{"@type":"RESEARCHMAP","@value":"https://researchmap.jp/eguchi"}],"foaf:name":[{"@value":"Koji Eguchi"}],"jpcoar:affiliationName":[{"@value":"Kobe University, Kobe, Japan"}]}],"publication":{"prism:publicationName":[{"@value":"Proceedings of the 26th International Conference on World Wide Web Companion  - WWW '17 Companion"}],"dc:publisher":[{"@value":"ACM Press"}],"prism:publicationDate":"2017","prism:startingPage":"1499","prism:endingPage":"1504"},"reviewed":"false","dc:rights":["https://creativecommons.org/licenses/by/4.0/"],"url":[{"@id":"https://dl.acm.org/doi/10.1145/3041021.3053905"},{"@id":"http://dl.acm.org/ft_gateway.cfm?id=3053905&ftid=1865284&dwn=1"}],"createdAt":"2018-01-11","modifiedAt":"2025-06-18","project":[{"@id":"https://cir.nii.ac.jp/crid/1040000781826352256","@type":"Project","projectIdentifier":[{"@type":"KAKEN","@value":"15H02703"},{"@type":"JGN","@value":"JP15H02703"},{"@type":"URI","@value":"https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-15H02703/"}],"notation":[{"@language":"ja","@value":"潜在変数モデルの逐次推定に基づく大規模複雑データ解析"},{"@language":"en","@value":"Large-scale data analysis based on latent variable models and sequential estimation methods"}]}],"relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360282588967085056","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Sequential Monte Carlo Methods in Practice"}]},{"@id":"https://cir.nii.ac.jp/crid/1360292620785602432","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Clustering in weighted networks"}]},{"@id":"https://cir.nii.ac.jp/crid/1362825893286138240","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Estimation and Prediction for Stochastic Blockstructures"}]},{"@id":"https://cir.nii.ac.jp/crid/1363388845541515776","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Finding scientific topics"}]},{"@id":"https://cir.nii.ac.jp/crid/1363670319890874496","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure"}]},{"@id":"https://cir.nii.ac.jp/crid/1364233270509127552","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"A Survey of Statistical Network Models"}]},{"@id":"https://cir.nii.ac.jp/crid/1390001204377778688","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Online Inference of Mixed Membership Stochastic Blockmodels for Network Data Streams"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1145/3041021.3053905"},{"@type":"KAKEN","@value":"PRODUCT-21698731"},{"@type":"OPENAIRE","@value":"doi_dedup___::e053647121888e1b50b195ec3d8374f8"}]}