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Peer-to-Peer Networking and Applications
Erschienen in: Peer-to-Peer Networking and Applications 6/2017

01.02.2016

Events detection and community partition based on probabilistic snapshot for evolutionary social network

verfasst von: Zhongnan Zhang, Lei Hu, Ming Qiu, Fangyuan Gao

Erschienen in: Peer-to-Peer Networking and Applications | Ausgabe 6/2017

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Abstract

Most of the existing researches simply convert associations of nodes within the snapshot of the evolutionary social network to the weight of edges. However, because of the obvious Matthew effect existing in the interactions of nodes in the real social network, the association strength matrices extracted directly by snapshots are extremely uneven. This paper introduces a new evolutionary social network model. Firstly, we generate probabilistic snapshots of the evolutionary social network data. Afterwards, we use the probabilistic factor model to detect the variation points brought by network events. Finally we partition the network community based on snapshots with stable structures before and after the variation points. According to experimental results, our proposed probabilistic snapshot model is effective for network events detection and network community partition.
Vorheriger Artikel Editor’s note: Special Issue on Mobile Ad-hoc and Sensor Networks
Nächster Artikel Fault diagnosis of body sensor networks using hidden Markov model

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Metadaten
Titel
Events detection and community partition based on probabilistic snapshot for evolutionary social network
verfasst von
Zhongnan Zhang
Lei Hu
Ming Qiu
Fangyuan Gao
Publikationsdatum
01.02.2016
Verlag
Springer US
Erschienen in
Peer-to-Peer Networking and Applications / Ausgabe 6/2017
Print ISSN: 1936-6442
Elektronische ISSN: 1936-6450
DOI
https://doi.org/10.1007/s12083-016-0427-6

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