Discriminative Frequent Pattern-Based Graph Classification

Frequent graph mining has been studied extensively with many scalable graph mining algorithms developed in the past. Graph patterns are essential not only for exploratory graph mining but also for advanced graph analysis tasks such as graph indexing, graph clustering, and graph classification. In this chapter, we examine the frequent pattern-based classification of graph data. We will introduce different types of patterns used in graph classification and their efficient mining approaches. These approaches could directly mine the most discriminative subgraphs without enumerating the complete set of frequent graph patterns. The application of graph classification into chemical compound analysis and software behavior prediction will be discussed to demonstrate the power of discriminative subgraphs.

[1]  Hans-Peter Kriegel,et al.  Shortest-path kernels on graphs , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[2]  Philip S. Yu,et al.  Direct Discriminative Pattern Mining for Effective Classification , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[3]  Jade Goldstein-Stewart,et al.  The use of MMR, diversity-based reranking for reordering documents and producing summaries , 1998, SIGIR '98.

[4]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[5]  Koji Tsuda,et al.  Entire regularization paths for graph data , 2007, ICML '07.

[6]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[7]  Jan Ramon,et al.  Expressivity versus efficiency of graph kernels , 2003 .

[8]  Jiawei Han,et al.  CPAR: Classification based on Predictive Association Rules , 2003, SDM.

[9]  Hisashi Kashima,et al.  Marginalized Kernels Between Labeled Graphs , 2003, ICML.

[10]  George Karypis,et al.  Frequent Substructure-Based Approaches for Classifying Chemical Compounds , 2005, IEEE Trans. Knowl. Data Eng..

[11]  Philip S. Yu,et al.  Mining significant graph patterns by leap search , 2008, SIGMOD Conference.

[12]  Philip S. Yu,et al.  Direct mining of discriminative and essential frequent patterns via model-based search tree , 2008, KDD.

[13]  Alessandro Orso,et al.  Rapid: Identifying Bug Signatures to Support Debugging Activities , 2008, 2008 23rd IEEE/ACM International Conference on Automated Software Engineering.

[14]  Mohammed J. Zaki,et al.  Lazy Associative Classification , 2006, Sixth International Conference on Data Mining (ICDM'06).

[15]  Thomas Gärtner,et al.  Cyclic pattern kernels for predictive graph mining , 2004, KDD.

[16]  Ambuj K. Singh,et al.  GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[17]  George Karypis,et al.  Frequent subgraph discovery , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[18]  Luc De Raedt,et al.  Molecular feature mining in HIV data , 2001, KDD '01.

[19]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[20]  Andreas Zell,et al.  Optimal assignment kernels for attributed molecular graphs , 2005, ICML.

[21]  Jiawei Han,et al.  Discriminative Frequent Pattern Analysis for Effective Classification , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[22]  George Karypis,et al.  Comparison of descriptor spaces for chemical compound retrieval and classification , 2006, Sixth International Conference on Data Mining (ICDM'06).

[23]  Chao Liu,et al.  Mining Behavior Graphs for "Backtrace" of Noncrashing Bugs , 2005, SDM.

[24]  Thomas Gärtner,et al.  On Graph Kernels: Hardness Results and Efficient Alternatives , 2003, COLT.

[25]  R. Mike Cameron-Jones,et al.  FOIL: A Midterm Report , 1993, ECML.

[26]  Jianyong Wang,et al.  HARMONY: Efficiently Mining the Best Rules for Classification , 2005, SDM.

[27]  Anthony K. H. Tung,et al.  Mining top-K covering rule groups for gene expression data , 2005, SIGMOD '05.

[28]  Yuji Matsumoto,et al.  An Application of Boosting to Graph Classification , 2004, NIPS.

[29]  Jiawei Han,et al.  CloseGraph: mining closed frequent graph patterns , 2003, KDD '03.

[30]  Philip S. Yu,et al.  Near-optimal Supervised Feature Selection among Frequent Subgraphs , 2009, SDM.

[31]  Hong Cheng,et al.  Identifying bug signatures using discriminative graph mining , 2009, ISSTA.