Hostname: page-component-745bb68f8f-g4j75 Total loading time: 0 Render date: 2025-01-08T07:59:21.956Z Has data issue: false hasContentIssue false
  • English
  • Français

A survey of frequent subgraph mining algorithms

Published online by Cambridge University Press:  20 November 2012

Chuntao Jiang ,
Frans Coenen  and
Michele Zito
Chuntao Jiang
Affiliation:
Department of Computer Science, The University of Liverpool, Ashton Building, Ashton Street, Liverpool L69 3BX, UK; e-mail: [email protected], [email protected], [email protected]
Frans Coenen
Affiliation:
Department of Computer Science, The University of Liverpool, Ashton Building, Ashton Street, Liverpool L69 3BX, UK; e-mail: [email protected], [email protected], [email protected]
Michele Zito
Affiliation:
Department of Computer Science, The University of Liverpool, Ashton Building, Ashton Street, Liverpool L69 3BX, UK; e-mail: [email protected], [email protected], [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective. This paper presents a survey of current research in the field of frequent subgraph mining and proposes solutions to address the main research issues.

Type
Articles
Information
The Knowledge Engineering Review , Volume 28 , Issue 1 , March 2013 , pp. 75 - 105
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abello, A., Resende, M. G. C., Sundarsky, S. 2002. Massive quasi-clique detection. In Proceedings of the 5th Latin America Symposium on Theoretical Informatics, Cancun, Mexico, 598–612.Google Scholar
Agrawal, R., Srikant, R. 1994. Fast algorithm for mining association rules. In Proceedings of the 20th International Conference on Very Large Databases (VLDB). Morgan Kaufmann, 487–499.Google Scholar
Agrawal, R. C., Aggarwal, C. C., Prasad, V. V. V. 2001. A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing 61(3), 350–371.CrossRefGoogle Scholar
Alm, E., Arkin, A. P. 2003. Biological Networks. Current Opinion in Structural Biology 13(2), 193–202.CrossRefGoogle ScholarPubMed
Asai, T., Abe, K., Kawasoe, S., Arimura, H., Satamoto, H., Arikawa, S. 2002. Efficient substructure discovery from large semi-structured data. In Proceedings of the 2nd SIAM International Conference on Data Mining, Fukuoka, Japan, 158–174.Google Scholar
Asai, T., Arimura, H., Uno, T., Nakano, S. 2003. Discovering frequent substructures in large unordered trees. In Proceedings of the 6th International Conference on Discovery Science, Fukuoka, Japan, 47–61.Google Scholar
Bayardo, R. J. Jr 1998. Efficiently Mining Long Patterns from Databases. In Proceedings of the 1998 International Conference on Management of Data, 85–93.CrossRefGoogle Scholar
Bomze, I. M., Budinich, M., Pardalos, P. M., Pelillo, M. 1999. The maximum clique problem. In Handbook of Combinatorial Optimization, Do, D-Z. & Pardalos, P. M. (eds). Kluwer Academic Publishers, 4, 1–74.Google Scholar
Borgelt, C., Berthold, M. 2002. Mining molecular fragments: finding relevant substructures of molecules. In Proceedings of International Conference on Data Mining, 211–218.Google Scholar
Borgwardt, K. M., Kriegel, H. P. 2005. Shortest-path kernels on graphs. In Proceedings of the 2005 International Conference on Data Mining, 74–81.Google Scholar
Brin, S., Page, L. 1998. The anatomy of a large-scale hyper-textual web search engine. In Proceedings of the 7th International World Wide Web Conference, 107–117.Google Scholar
Bunke, H., Allerman, G. 1983. Inexact graph matching for structural pattern recognition. Pattern Recognition Letters 1(4), 245–253.CrossRefGoogle Scholar
Calders, T., Ramon, J., van Dyck, D. 2008. Anti-monotonic overlap-graph support measures. In Proceedings of the Eighth IEEE International Conference on Data Mining, 73–82.Google Scholar
Chakrabarti, S., Dom, B., Gibson, D., Kleinberg, J., Kumar, R., Raghavan, P., Rajagopaln, S., Tomkins, A. 1999. Mining the link structure of the world wide web. IEEE Computer 32(8), 60–67.CrossRefGoogle Scholar
Chen, M. S., Han, J., Yu, P. S. 1996. Data mining: an overview from database perspective. IEEE Transaction on Knowledge and Data Engineering 8, 866–883.CrossRefGoogle Scholar
Chen, C., Yan, X., Zhu, F., Han, J. 2007a. gApprox: mining frequent approximate patterns from a massive network. In Proceedings of the 7th IEEE International Conference on Data Mining, 445–450.Google Scholar
Chen, C., Yan, X., Yu, P. S., Han, J., Zhang, D., Gu, X. 2007b. Towards graph containment search and indexing. In Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB'07), 926–937.Google Scholar
Chen, C., Lin, C. X., Yan, X., Han, J. 2008. On effective presentation of graph patterns: a structural representative approach. In Proceedings of the 17th ACM Conference on Information and Knowledge Management, 299–308.Google Scholar
Chi, Y., Yang, Y., Xia, Y., Muntz, R. R. 2003. Indexing and mining free trees. In Proceedings of the 2003 IEEE International Conference on Data Mining, 509–512.Google Scholar
Chi, Y., Nijssen, S., Muntz, R., Kok, J. 2004. Frequent subtree mining – an overview. Fundamenta Informaticae, Special Issue on Graph and Tree Mining 66(1–2), 161–198.Google Scholar
Chi, Y., Yang, Y., Xia, Y., Muntz, R. R. 2004a. HybridTreeMiner: an efficient algorithm for mining frequent rooted trees and trees using canonical forms. In Proceedings of the 16th International Conference on Scientific and Statistical Database Management, 11–20.Google Scholar
Chi, Y., Yang, Y., Xia, Y., Muntz, R. R. 2004b. CMTreeMiner: mining both closed and maximal frequent subtrees. In Proceedings of the 8th Pacific Asia Conference on Knowledge Discovery and Data Mining, 63–73.Google Scholar
Chi, Y., Yang, Y., Xia, Y., Muntz, R. R. 2005. Canonical forms for labelled trees and their applications in frequent subtree mining. Journal of Knowledge and Information Systems 8(2), 203–234.CrossRefGoogle Scholar
Christmas, W. J., Kittler, J., Petrou, M. 1995. Structural matching in computer vision using probabilistic relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(8), 749–764.CrossRefGoogle Scholar
Chung, J. C. 1987. $$\[--><$> {\scr O}<$><!--$$(n 2.5) time algorithm for subgraph homeomorphism problem on trees. Journal of Algorithms 8, 106–112.CrossRefGoogle Scholar
Conte, D., Foggia, F., Sansone, C., Vento, M. 2004. Thirty years of graph matching in pattern recognition. International Journal of Pattern Recognition and Artificial Intelligence 18(3), 265–298.CrossRefGoogle Scholar
Cook, D. J., Holder, L. B. 1994. Substructure discovery using minimum description length and background knowledge. Journal of Artificial Intelligence Research 1, 231–255.CrossRefGoogle Scholar
Cook, D. J., Holder, L. B. 2000. Graph-based data mining. IEEE Intelligent Systems 15(2), 32–41.CrossRefGoogle Scholar
Cordella, L. P., Foggia, P., Sansone, C., Vento, M. 2001. An improved algorithm for matching large graphs. In Proceedings of the 3rd IAPR-TC15 Workshop on Graph-based Representation in Pattern Recognition, 149–159.Google Scholar
Cordella, L. P., Foggia, P., Sansone, C., Tortorella, F., Vento, M. 1998. Graph Matching: a fast algorithm and its evaluation. In Proceedings of the 14th Conference on Pattern Recognition, 1582–1584.Google Scholar
Cui, J. H., Kim, J., Maggiorini, D., Boussetta, K., Gerla, M. 2005. Aggregated multicast – a comparative study. Cluster Computing 8(1), 15–26.CrossRefGoogle Scholar
Deshpande, M., Kuramochi, M., Wale, N., Karypis, G. 2005. Frequent sub-structure-based approach for classifying chemical compounds. IEEE Transactions on Knowledge and Data Engineering 17(8), 1036–1050.CrossRefGoogle Scholar
Fan, W., Zhang, K., Cheng, H., Gao, J., Yan, X., Han, J., Yu, P. S., Verscheure, O. 2008. Direct mining of discriminative and essential frequent patterns via model-based search tree. In Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, USA, 230–238.Google Scholar
Fatta, G. D., Berthold, M. R. 2005. High performance subgraph mining in molecular compounds. In Proceedings of the 2005 International Conference on High Performance Computing and Communications (HPCC'05), 866–877.Google Scholar
Fischer, I., Meinl, T. 2004. Graph based molecular data mining – an overview. In Proceedings of the 2004 IEEE International Conference on Systems,Man and Cybernetics, 4578–4582.Google Scholar
Flake, G., Tarjan, R., Tsioutsiouliklis, K. 2004. Graph clustering and minimum cut trees. Internet Mathematics 1, 385–408.CrossRefGoogle Scholar
Fortin, S. 1996. The Graph Isomorphism Problem Technical report, no. TR06-20, The University of Alberta.Google Scholar
Foggia, P., Genna, R., Vento, M. 2001. A performance comparison of five algorithms for graph isomorphism. In Proceedings of the 3rd IAPR-TC15 Workshop on Graph-based Representation in Pattern Recognition, 188–199.Google Scholar
Garey, M. R., Johnson, D. S. 1979. Computers and intractability – a guide to the theory of NP-completeness. W.H. Freeman and Company.Google Scholar
Gärtner, T., Flach, P., Wrobel, S. 2003. On graph kernels: hardness results and efficient alternatives. In Proceedings of the 16th Annual Conference on Learning Theory (COLT'03), 129–143.Google Scholar
Getoor, L., Diehl, C. 2005. Link mining: a survey. ACM SIGKDD Explorations Newsletter 7(2), 3–12.CrossRefGoogle Scholar
Gibbons, A. 1985. Algorithmic Graph Theory. Cambridge University Press.Google Scholar
Greco, G., Guzzo, A., Manco, G., Pontieri, L., Saccá, D. 2005. Mining Constrained Graphs: the case of workflow systems, constraint based mining and inductive databases, Lecture Notes in Computer Science, 155–171. Springer.CrossRefGoogle Scholar
Gudes, E., Shimony, S. E., Vanetik, N. 2006. Discovering frequent graph patterns using disjoint paths. IEEE Transaction on Knowledge and Data Engineering 18(11), 1441–1456.CrossRefGoogle Scholar
Gutin, G. 2004. 5.3 Independent sets and cliques. In Handbook of Graph Theory, Discrete Mathematics & Its Applications, Gross, J. L. & Yellin, J. (eds). CRC Press, 389–402.Google Scholar
Han, J., Kamber, M. 2006. Data Mining Concepts and Techniques, 2nd edition. Morgan Kaufmann.Google Scholar
Han, J., Pei, J., Yin, Y. 2000. Mining frequent patterns without candidate generation. In Proceedings of ACM SIGMOD International Conference on Management of Data, 1–12.Google Scholar
Han, J., Cheng, H., Xin, D., Yan, X. 2007. Frequent pattern mining: current status and future directions. Journal of Data Mining and Knowledge Discovery 15(1), 55–86.CrossRefGoogle Scholar
Harary, F., Ross, I. C. 1957. A procedure for clique detection using the group matrix. Sociometry 20(3), 205–215.CrossRefGoogle Scholar
Hein, J., Jiang, T., Wang, L., Zhang, K. 1996. On the complexity of comparing evolutionary trees. Discrete Applied Mathematics 71(1–3), 153–169.CrossRefGoogle Scholar
Hido, S., Kawano, H. 2005. AMIOT:induced ordered tree mining in tree-structured databases. In Proceedings of the 5th IEEE International Conference on Data Mining, 170–177.Google Scholar
Hopcroft, J. E., Tarjan, R. E. 1972. Isomorphism of planar graphs. In Complexity of Computer Computations, Miller, R. E. & Thatcher, J. W. (eds). IBM Research Symposia Series Plenum Press, 131–152.CrossRefGoogle Scholar
Hu, H., Yan, X., Huang, Y., Han, J., Zhou, X. 2005. Mining coherent dense subgraphs across massive biological networks for functional discovery. Bioinformatics 21(1), 213–221.CrossRefGoogle ScholarPubMed
Huan, J., Wang, W., Prins, J. 2003. Efficient mining of frequent subgraph in the presence of isomorphism. In Proceedings of the 2003 International Conference on Data Mining, 549–552.Google Scholar
Huan, J., Wang, W., Prins, J., Yang, J. 2004. SPIN: mining maximal frequent subgraphs from graph databases. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 581–586.Google Scholar
Huang, X., Lai, W. 2006. Clustering graphs for visualization via node similarities. Visual Language and Computing 17, 225–253.CrossRefGoogle Scholar
Inokuchi, A., Washio, T., Motoda, H. 2000. An Apriori-based algorithm for mining frequent substructures from graph data. In Proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, 13–23.Google Scholar
Inokuchi, A., Washio, T., Motoda, H. 2003. Complete mining of frequent patterns from graphs: mining graph data. Journal of Machine Learning 50(3), 321–354.CrossRefGoogle Scholar
Inokuchi, A., Washio, T., Nishimura, K., Motoda, H. 2002. A Fast Algorithm for Mining Frequent Connected Subgraphs. Technical report RT0448, IBM Research, Tokyo Research Laboratory, Japan.Google Scholar
Jahn, K., Kramer, S. 2005. Optimizing gSpan for molecular datasets. In Proceedings of the 3rd International Workshop on Mining Graphs, Trees and Sequences, 509–523.Google Scholar
Kashima, H., Tsuda, K., Inokuchi, A. 2003. Marginalized kernels between labelled graphs. In Proceedings of the 20th International Conference on Machine Learning (ICML'03), 321–328.Google Scholar
Ke, Y., Cheng, J.,, Ng, W. 2007. Correlated search in graph databases. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 390–399.Google Scholar
Ke, Y., Cheng, J., Yu, J. 2009. Efficient discovery of frequent correlated subgraph pairs. In Proceedings of the 9th IEEE International Conference on Data Mining, 239–248.Google Scholar
Kelley, B., Sharan, R., Karp, R., Sittler, E., Root, D., Stockwell, B., Tdeker, T. 2003. Conserved pathways within bacteria and yeast as revealed by Global Protein Network alignment. In Proceedings of the National Academy of Science of the United States of America (PNAS'03) 100(20), 11394–11399.Google Scholar
Kleinberg, J. M. 1998. Authoritative sources in a hyper-linked environment. In Proceedings of ACM-SIAM Symposium Discrete Algorithms, 668–677.Google Scholar
Kosala, R., Blockeel, H. 2000. Web mining research: a survey. ACM SIGKDD Explorations Newsletter 2(1), 1–15.CrossRefGoogle Scholar
Koyutürk, M., Grama, A., Szpankowski, W. 2004. An efficient algorithm for detecting frequent subgraphs in biological networks. Journal of Bioinformatics 20(1), 200–207.CrossRefGoogle ScholarPubMed
Kudo, T., Maeda, E., Matsumoto, Y. 2004. An application to boosting to graph classification. In Proceedings of the 8th Annual Conference on Neural Information Processing Systems, 729–736.Google Scholar
Kuramochi, M., Karypis, G. 2001. Frequent subgraph discovery. In Proceedings of the International Conference on Data Mining, 313–320.Google Scholar
Kuramochi, M., Karypis, G. 2002. Discovering frequent geometric subgraphs. In Proceedings of the IEEE International Conference on Data Mining, 258–265.Google Scholar
Kuramochi, M., Karypis, G. 2004a. An efficient algorithm for discovering frequent subgraphs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1038–1051.CrossRefGoogle Scholar
Kuramochi, M., Karypis, G. 2004b. GREW-A scalable frequent subgraph discovery algorithm. In Proceedings of the 4th IEEE International Conference on Data Mining, 439–442.Google Scholar
Kuramochi, M., Karypis, G. 2004c. Finding frequent patterns in a large sparse graph. In Proceedings of the SIAM International Conference on Data Mining, 345–356.Google Scholar
Kuramochi, M., Karypis, G. 2005. Finding frequent patterns in a large sparse graph. Data Mining and Knowledge Discovery 11(3), 243–271.CrossRefGoogle Scholar
Liu, B. 2008. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer.Google Scholar
Liu, T. L., Geiger, D. 1999. Approximate tree matching and shape similarity. In Proceedings of 7th International Conference on Computer Vision, 456–462.Google Scholar
Matula, D. W. 1978. Subtree isomorphism in $$\[--><$> {\scr O}<$><!--$$(n 5/2) Annals of Discrete Mathematics 2, 91–106.CrossRefGoogle Scholar
McKay, B. D. 1981. Practical graph isomorphism. Congressus Numerantium 30, 45–87.Google Scholar
Messmer, B. T., Bunke, H. 1998. A new algorithm for error-tolerant subgraph isomorphism detection. IEEE Transaction on Pattern Analysis and Machine Intelligence 20(5), 493–504.CrossRefGoogle Scholar
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U. 2002. Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827.CrossRefGoogle ScholarPubMed
Miyazaki, T. 1997. The complexity of McKay's canonical labelling algorithm, Groups and Computation II, DIMACS Series Discrete Mathematics Theoretical Computer Science, American Mathematical Society, 28, 239–256.Google Scholar
Newman, M. E. J. 2004. Detecting community structure in networks. The European Physical Journal B – Condensed Matter and Complex Systems 38(2), 321–330.CrossRefGoogle Scholar
Nijssen, S., Kok, J. N. 2003. Efficient discovery of frequent unordered trees. In Proceedings of the 1st International Workshop on Mining Graphs, Trees and Sequences, 55–64.Google Scholar
Nijssen, S., Kok, J. N. 2004. A quickstart in frequent structure mining can make a difference. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 647–652.Google Scholar
Ozaki, T., Ohkawa, T. 2008. Mining correlated subgraphs in graph databases. In Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'08), 272–283.Google Scholar
Paul, S. 1998. Multicasting on the Internet and Its Applications. Kluwer Academic Publishers.CrossRefGoogle Scholar
Pei, J., Jiang, D., Zhang, A. 2005. On mining cross-graph quasi-cliques. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, USA, 228–238.Google Scholar
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M. C. 2001. PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In Proceedings of 12th IEEE International Conference on Data Engineering (ICDE 01), Heidelberg, Germany, 215–224.Google Scholar
Preiss, B. R. 1998. Data Structures and Algorithms with Object-Oriented Design Patterns in C++. Wiley.Google Scholar
Prüfer, H. 1918. Neuer beweis eines satzes über permutationen. Archiv für Mathematik und Physik 27, 742–744.Google Scholar
Read, R. C., Corneil, D. G. 1977. The graph isomorph disease. Journal of Graph Theory 1, 339–363.CrossRefGoogle Scholar
Rückert, U., Kramer, S. 2004. Frequent free tree discovery in graph data. In Proceedings of Special Track on Data Mining, ACM Symposium on Applied Computing, 564–570.Google Scholar
Schmidt, D. C., Druffel, L. E. 1976. A fast backtracking algorithm to test directed graphs for isomorphism using distance matrices. Journal of the ACM 23(3), 433–445.CrossRefGoogle Scholar
Schreiber, F., Schwöbbermeyer, H. 2005. Frequency concepts and pattern detection for the analysis of motifs in networks. Transactions on Computational Systems Biology 3, 89–104.CrossRefGoogle Scholar
Shamir, R., Tsur, D. 1999. Faster subtree isomorphism. Journal of Algorithms 33(2), 267–280.CrossRefGoogle Scholar
Shapiro, L. G., Haralick, R. M. 1981. Structural descriptions and inexact matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 3, 504–519.CrossRefGoogle ScholarPubMed
Shasha, D., Wang, J. T. L., Giugno, R. 2002. Algorithms and applications of tree and graph searching. In Proceedings of the 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles on Database Systems, 39–52.Google Scholar
Shasha, D., Wang, J., Zhang, S. 2004. Unordered tree mining with applications to phylogeny. In Proceedings of the 20th International Conference on Data Engineering (ICDE 04), 708–719.Google Scholar
Sharan, R., Suthram, S., Kelley, R., Kuhn, T., McCuine, S., Uetz, P., Sittler, T., Karp, R., Ideker, T. 2005. Conserved patterns of protein interaction in multiple species. In Proceedings of the National Academy of Science of the United States of America (PNAS'05), 102(6), 1974–1979.Google Scholar
Tan, H., Dillon, T. S., Feng, L., Chang, E., Hadzic, F. 2005. X3-Miner: mining patterns from XML database. In Proceedings of the 6th International Data Mining, 287–297.Google Scholar
Tan, H., Dillon, T. S., Hadzic, F., Chang, E., Feng, L. 2006. IMB3-Miner: mining induced/embedded subtrees by constraining the level of embedding. In Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 450–461.Google Scholar
Tatikonda, S., Parthasarathy, S., Kurc, T. 2006. Trips and Tides: new algorithms for tree mining. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management, 455–464.Google Scholar
Termier, A., Rousset, M. C., Sebag, M. 2002. Treefinder: a first step towards XML data mining. In Proceedings of the 2002 IEEE International Conference on Data Mining, 450–457.Google Scholar
Thomas, L. T., Valluri, S. R., Karlapalem, K. 2006. MARGIN: maximal frequent subgraph mining. In Proceedings of the 6th International Conference on Data Mining (ICDM 06), Hong Kong, 1097–1101.Google Scholar
Ullmann, J. R. 1976. An algorithm for subgraph isomorphism. Journal of the ACM 23(1), 31–42.CrossRefGoogle Scholar
Valiente, G. 2002. Algorithms on Trees and Graphs. Springer.CrossRefGoogle Scholar
Vanetik, N. 2002. Discovery of Frequent Patterns in Semi-structured Data. Department of Computer Science, Ben Gurion University.Google Scholar
Vanetik, N., Gudes, E., Shimony, S. E. 2002. Computing frequent graph patterns from semi-structured data. In Proceedings of the 2nd International Conference on Data Mining, 458–465.Google Scholar
Vanetik, N., Shimony, S. E., Gudes, E. 2006. Support measures for graph data. Journal of Data Mining and Knowledge Discovery 13(2), 243–260.CrossRefGoogle Scholar
Wang, C., Hong, M., Pei, J., Zhou, H., Wang, W., Shi, B. 2004a. Efficient pattern-growth methods for frequent tree pattern mining. In Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 441–451.Google Scholar
Wang, C., Wang, W., Pei, J., Zhu, Y., Shi, B. 2004b. Scalable mining of large disk-based graph databases. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 316–325.Google Scholar
Wang, J., Zeng, Z., Zhou, L. 2006. CLAN: an algorithm for mining closed cliques from large dense graph databases. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, USA, 797–802.Google Scholar
Washo, T., Motoda, H. 2003. State of the art of graph-based data mining. SIGKDD Explorations 5, 59–68.CrossRefGoogle Scholar
West, D. B. 2000. Introduction to Graph Theory, 2nd edition. Prentice Hall.Google Scholar
Wörlein, M., Meinl, T., Fischer, I., Philippsen, M. 2005. A quantitative comparison of the subgraph miners MoFa, gSpan, FFSM and Gaston. In Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, 392–404.Google Scholar
Xin, D., Cheng, H., Yan, X., Han, J. 2006. Extracting redundancy aware top K patterns. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 444–453.Google Scholar
Yan, X., Han, J. W. 2002. gSpan: graph-based substructure pattern mining. In Proceedings of the International Conference on Data Mining, 721–724.Google Scholar
Yan, X., Han, J. 2003. CloseGraph: mining closed frequent graph patterns. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D.C., USA, 286–295.Google Scholar
Yan, X., Yu, P. S., Han, J. 2004. Graph Indexing: a frequent structure-based approach. In Proceedings of ACM-SIGMOD International Conference on Management of Data, Paris, France, 335–346.Google Scholar
Yan, X., Zhou, X., Han, J. 2005a. Mining closed relational graphs with connectivity constraints. In Proceeding of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 324–333.Google Scholar
Yan, X., Yu, P. S., Han, J. 2005b. Sub-structure similarity search in graph databases. In Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, 766–777.Google Scholar
Yan, X., Zhu, F., Han, J., Yu, P. S. 2006. Searching substructures with superimposed distance. In Proceedings of the 22nd International Conference on Data Engineering, 88–97.Google Scholar
Yan, X., Cheng, H., Han, J., Yu, P. S. 2008. Mining significant graph patterns by leap search. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, 433–444.Google Scholar
Zaki, M. J. 2002. Efficiently Mining Frequent Trees in a Forest. In Proceedings of the SIGKDD 2002. ACM, 71–80.Google Scholar
Zaki, M. J. 2005a. Efficiently mining frequent embedded unordered trees. Fundamenta Informaticae 66(1–2), 33–52.Google Scholar
Zaki, M. J. 2005b. Efficiently mining frequent trees in a forest: algorithms and applications. IEEE Transactions on Knowledge and Data Engineering 17(8), 1021–1035.CrossRefGoogle Scholar
Zaki, M. J., Aggarwal, C. C. 2003. XRules: an effective structural classifier for XML data. In Proceedings of the 2003 International Conference on Knowledge Discovery and Data Mining, 316–325.Google Scholar
Zeng, Z., Wang, J., Zhou, L., Karypis, G. 2006. Coherent closed quasi-clique discovery from large dense graph databases. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, USA, 797–802.Google Scholar
Zhang, S., Wang, J. T. L. 2006. Mining frequent agreement subtrees in phylogenetic databases. In Proceedings of the 6th SIAM International Conference on Data Mining, 222–233.Google Scholar
Zhang, S., Yang, J. 2008. RAM: Randomized Approximate Graph Mining. In Proceedings of the 20th International Conference on Scientific and Statistical Database Management, 187–203.Google Scholar
Zhao, P., Yu, J. 2006. Fast frequent free tree mining in graph databases. In Proceedings of the 6th IEEE International Conference on Data Mining Workshop, 315–319.Google Scholar
Zhao, P., Yu, J. 2007. Mining closed frequent free trees in graph databases. In Proceedings of the 12th International Conference on Database Systems for Advanced Applications, Thailand, 91–102.Google Scholar
Zhu, F., Yan, X., Han, J., Yu, P. S. 2007. gPrune: a constraint pushing framework for graph pattern mining. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, 388–400.Google Scholar