Local Motif Clustering on Time-Evolving Graphs

Dongqi Fu, Dawei Zhou, Jingrui He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Graph motifs are subgraph patterns that occur in complex networks, which are of key importance for gaining deep insights into the structure and functionality of the graph. Motif clustering aims at finding clusters consisting of dense motif patterns. It is commonly used in various application domains, ranging from social networks to collaboration networks, from market-basket analysis to neuroscience applications. More recently, local clustering techniques have been proposed for motif-aware clustering, which focuses on a small neighborhood of the input seed node instead of the entire graph. However, most of these techniques are designed for static graphs and may render sub-optimal results when applied to large time-evolving graphs. To bridge this gap, in this paper, we propose a novel framework, Local Motif Clustering on Time-Evolving Graphs (L-MEGA), which provides the evolution pattern of the local motif cluster in an effective and efficient way. The core of L-MEGA is approximately tracking the temporal evolution of the local motif cluster via novel techniques such as edge filtering, motif push operation, and incremental sweep cut. Furthermore, we theoretically analyze the efficiency and effectiveness of these techniques on time-evolving graphs. Finally, we evaluate the L-MEGA framework via extensive experiments on both synthetic and real-world temporal networks.

Original languageEnglish (US)
Title of host publicationKDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages390-400
Number of pages11
ISBN (Electronic)9781450379984
DOIs
StatePublished - Aug 23 2020
Event26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020 - Virtual, Online, United States
Duration: Aug 23 2020Aug 27 2020

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
CountryUnited States
CityVirtual, Online
Period8/23/208/27/20

Keywords

  • high-order structure
  • local clustering
  • time-evolving graph

ASJC Scopus subject areas

  • Software
  • Information Systems

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