Motif-Preserving Dynamic Local Graph Cut
2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)(2018)
摘要
Modeling and characterizing high-order connectivity patterns are essential for understanding many complex systems, ranging from social networks to collaboration networks, from finance to neuroscience. However, existing works on high-order graph clustering assume that the input networks are static. Consequently, they fail to explore the rich high-order connectivity patterns embedded in the network evolutions, which may play fundamental roles in real applications. For example, in financial fraud detection, detecting loops formed by sequenced transactions helps identify money laundering activities; in emerging trend detection, star-shaped structures showing in a short burst may indicate novel research topics in citation networks. In this paper, we bridge this gap by proposing a local graph clustering framework that captures structure-rich subgraphs, taking into consideration the information of high-order structures in temporal networks. In particular, our motif-preserving dynamic local graph cut framework (MOTLOC) is able to model various user-defined temporal network structures and find clusters with minimum conductance in a polylogarithmic time complexity. Extensive empirical evaluations on synthetic and real networks demonstrate the effectiveness and efficiency of our MOTLOC framework.
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关键词
MOTLOC framework,polylogarithmic time complexity,user-defined temporal network structures,motif-preserving dynamic local graph cut framework,temporal networks,structure-rich subgraphs,local graph clustering framework,citation networks,star-shaped structures,trend detection,money laundering activities,sequenced transactions,financial fraud detection,rich high-order connectivity patterns,high-order graph clustering,collaboration networks,social networks
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