Multi-Graph-View learning for graph classification

Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Zhihua Cai, Chengqi Zhang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

35 Citations (Scopus)


Graph classification has traditionally focused on graphs generated from a single feature view. In many applications, it is common to have useful information from different channels/views to describe objects, which naturally results in a new representation with multiple graphs generated from different feature views being used to describe one object. In this paper, we formulate a new Multi-Graph-View learning task for graph classification, where each object to be classified contains graphs from multiple graph-views. This problem setting is essentially different from traditional single-graph-view graph classification, where graphs are from one single feature view. To solve the problem, we propose a Cross Graph-View Sub graph Feature based Learning (gCGVFL) algorithm that explores an optimal set of sub graphs, across multiple graph-views, as features to represent graphs. Specifically, we derive an evaluation criterion to estimate the discriminative power and the redundancy of sub graph features across all views, and assign proper weight values to each view to indicate its importance for graph classification. The iterative cross graph-view sub graph scoring and graph-view weight updating form a closed loop to find optimal sub graphs to represent graphs for multi-graph-view learning. Experiments and comparisons on real-world tasks demonstrate the algorithm's performance.

Original languageEnglish
Title of host publicationProceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
EditorsRavi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages10
ISBN (Electronic)9781479943029, 9781479943036
Publication statusPublished - 2014
Externally publishedYes
EventIEEE International Conference on Data Mining 2014 - Shenzhen, China
Duration: 14 Dec 201417 Dec 2014
Conference number: 14th (Conference Proceedings)

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786
ISSN (Electronic)2374-8486


ConferenceIEEE International Conference on Data Mining 2014
Abbreviated titleICDM 2014
Internet address


  • Feature Selection
  • Graph Classification
  • Multi-Graph-View
  • Subgraph Mining

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