Mining attributestructure correlated patterns in large attributed graphs

Arlei Silva, Wagner Meira, Mohammed J. Zaki

Research output: Contribution to journalArticle

89 Citations (Scopus)

Abstract

In this work, we study the correlation between attribute sets and the occurrence of dense subgraphs in large attributed graphs, a task we call structural correlation pattern mining. A structural correlation pattern is a dense subgraph induced by a particular attribute set. Existing methods are not able to extract relevant knowledge regarding how vertex attributes interact with dense subgraphs. Structural correlation pattern mining combines aspects of frequent itemset and quasi-clique mining problems. We propose statistical significance measures that compare the structural correlation of attribute sets against their expected values using null models. Moreover, we evaluate the interestingness of structural correlation patterns in terms of size and density. An effcient algorithm that combines search and pruning strategies in the identification of the most relevant structural correlation patterns is presented. We apply our method for the analysis of three real-world attributed graphs: a collaboration, a music, and a citation network, verifying that it provides valuable knowledge in a feasible time.

Original languageEnglish
Pages (from-to)466-477
Number of pages12
JournalProceedings of the VLDB Endowment
Volume5
Issue number5
DOIs
Publication statusPublished - Jan 2012

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

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