Matching Structure and Semantics: A Survey on Graph-Based Pattern Matching

The task of matching patterns in graph-structured data has applications in such diverse areas as computer vision, biology, electronics, computer aided design, social networks, and intelligence analysis. Consequently, work on graph-based pattern matching spans a wide range of research communities. Due to variations in graph characteristics and application requirements, graph matching is not a single problem, but a set of related problems. This paper presents a survey of existing work on graph matching, describing variations among problems, general and specific solution approaches, evaluation techniques, and directions for further research. An emphasis is given to techniques that apply to general graphs with semantic characteristics.

[1]  Shinji Umeyama,et al.  An Eigendecomposition Approach to Weighted Graph Matching Problems , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Horst Bunke,et al.  Subgraph Isomorphism in Polynomial Time , 1995 .

[3]  Takashi Washio,et al.  State of the art of graph-based data mining , 2003, SKDD.

[4]  Sang Uk Lee,et al.  A comparative study on attributed relational gra matching algorithms for perceptual 3-D shape descriptor in MPEG-7 , 2004, MULTIMEDIA '04.

[5]  Sherry Marcus,et al.  Graph-based technologies for intelligence analysis , 2004, CACM.

[6]  Neil Immerman,et al.  A Visual Language for Querying and Updating Graphs , 2002 .

[7]  Lawrence B. Holder,et al.  An Emprirical Study of Domain Knowledge and Its Benefits to Substructure Discovery , 1997, IEEE Trans. Knowl. Data Eng..

[8]  Dekang Lin,et al.  An Information-Theoretic Definition of Similarity , 1998, ICML.

[9]  李幼升,et al.  Ph , 1989 .

[10]  John D. Lowrance,et al.  LAW: A Workbench for Approximate Pattern Matching in Relational Data , 2003, IAAI.

[11]  Nicola Guarino,et al.  OntoSeek: content-based access to the Web , 1999, IEEE Intell. Syst..

[12]  George Karypis,et al.  Finding Frequent Patterns in a Large Sparse Graph* , 2005, Data Mining and Knowledge Discovery.

[13]  Julian R. Ullmann,et al.  An Algorithm for Subgraph Isomorphism , 1976, J. ACM.

[14]  Edwin R. Hancock,et al.  Bayesian Graph Edit Distance , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Abraham Kandel,et al.  On the Minimum Common Supergraph of Two Graphs , 2000, Computing.

[16]  Dennis Shasha,et al.  GraphGrep: A fast and universal method for querying graphs , 2002, Object recognition supported by user interaction for service robots.

[17]  M. Wolverto,et al.  Software Supported Pattern Development in Intelligence Analysis , 2006, 2006 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety.

[18]  John D. Lowrance,et al.  Link Analysis Workbench , 2004 .

[19]  William J. Christmas,et al.  Structural Matching in Computer Vision Using Probabilistic Relaxation , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Lawrence B. Holder,et al.  Substructure Discovery Using Minimum Description Length and Background Knowledge , 1993, J. Artif. Intell. Res..

[21]  Mario Vento,et al.  A Performance Comparison of Five Algorithms for Graph Isomorphism , 2001 .

[22]  Edwin R. Hancock,et al.  Bayesian graph edit distance , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[23]  Yannis E. Ioannidis,et al.  Balancing histogram optimality and practicality for query result size estimation , 1995, SIGMOD '95.

[24]  A. Paone,et al.  Discrete Time Relaxation Based on Direct Quadrature Methods for Volterra Integral Equations , 1999, Computing.

[25]  Bart Goethals,et al.  Mining tree queries in a graph , 2005, KDD '05.

[26]  B. McKay.  nauty User ’ s Guide ( Version 2 . 4 ) , 1990 .

[27]  M. Chein,et al.  Conceptual graphs: fundamental notions , 1992 .

[28]  King-Sun Fu,et al.  Error-Correcting Isomorphisms of Attributed Relational Graphs for Pattern Analysis , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[29]  Neoklis Polyzotis,et al.  Statistical synopses for graph-structured XML databases , 2002, SIGMOD '02.

[30]  Albert-László Barabási,et al.  Evolution of Networks: From Biological Nets to the Internet and WWW , 2004 .

[31]  Horst Bunke,et al.  Error Correcting Graph Matching: On the Influence of the Underlying Cost Function , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Horst Bunke,et al.  A graph distance metric based on the maximal common subgraph , 1998, Pattern Recognit. Lett..

[33]  John F. Sowa,et al.  Conceptual Structures: Information Processing in Mind and Machine , 1983 .

[34]  J. A. Campbell,et al.  A Novel Algorithm for Matching Conceptual and Related Graphs , 1995, ICCS.

[35]  Thorsten Meinl,et al.  A Quantitative Comparison of the Subgraph Miners MoFa, gSpan, FFSM, and Gaston , 2005, PKDD.

[36]  Amit P. Sheth,et al.  Template Based Semantic Similarity for Security Applications , 2005, ISI.

[37]  Erkki Oja,et al.  Comparisons of attributed graph matching algorithms for computer vision , 1990 .

[38]  B Gallagher,et al.  The State of the Art in Graph-Based Pattern Matching , 2006 .

[39]  Hongjun Lu,et al.  Bloom Histogram: Path Selectivity Estimation for XML Data with Updates , 2004, VLDB.

[40]  Minos N. Garofalakis,et al.  Approximate Query Processing: Taming the TeraBytes , 2001, VLDB.

[41]  Dennis Shasha,et al.  Algorithmics and applications of tree and graph searching , 2002, PODS.

[42]  Robert M. Haralick,et al.  Structural Descriptions and Inexact Matching , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[43]  Phillip B. Gibbons,et al.  Approximate Query Processing: Taming the TeraBytes! A Tutorial , 2001 .