Mining Graph Patterns

  • Hong ChengEmail author
  • Xifeng Yan
  • Jiawei Han


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.


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


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Systems Engineering and Engineering ManagementThe Chinese University of Hong KongHong KongChina
  2. 2.Department of Computer ScienceUniversity of California at Santa BarbaraSanta BarbaraUSA
  3. 3.Department of Computer ScienceUniversity of Illinois at Urbana-ChampaignChampaignUSA

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