Mining graph patterns

Hong Cheng, Xifeng Yan, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Graph pattern mining becomes increasingly crucial to applications in a variety of domains including bioinformatics, cheminformatics, social network analysis, computer vision and multimedia. In this chapter, we first examine the existing frequent subgraph mining algorithms and discuss their computational bottleneck. Then we introduce recent studies on mining various types of graph patterns, including significant, representative and dense subgraph patterns. We also discuss the mining tasks in new problem settings such as a graph stream and an uncertain graph model. These new mining algorithms represent the state-of-the-art graph mining techniques: they not only avoid the exponential size of mining result, but also improve the applicability of graph patterns significantly.

Original languageEnglish (US)
Title of host publicationFrequent Pattern Mining
PublisherSpringer International Publishing
Pages307-338
Number of pages32
Volume9783319078212
ISBN (Electronic)9783319078212
ISBN (Print)3319078208, 9783319078205
DOIs
StatePublished - Jul 1 2014

Keywords

  • Apriori
  • Dense pattern
  • Frequent subgraph
  • Graph pattern
  • Graph stream
  • Representative pattern
  • Significant pattern
  • Uncertain graph

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Cheng, H., Yan, X., & Han, J. (2014). Mining graph patterns. In Frequent Pattern Mining (Vol. 9783319078212, pp. 307-338). Springer International Publishing. https://doi.org/10.1007/978-3-319-07821-2_13