A survey of frequent subgraph mining algorithms

Knowledge Engineering Review

Volumn 28, Issue 1, 2013, Pages 75-105

  Jiang, Chuntao ^a   Coenen, Frans ^a   Zito, Michele ^a  

^a UNIVERSITY OF LIVERPOOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONALLY EFFICIENT; EFFECTIVE MECHANISMS; FREQUENT SUBGRAPH MINING; FREQUENT SUBGRAPHS; GRAPH DATA; GRAPH MINING; RESEARCH GOALS; RESEARCH ISSUES; SUBGRAPHS;

ALGORITHMS; DATA MINING; SURVEYS;

RESEARCH;

EID: 84874159239     PISSN: 02698889     EISSN: 14698005     Source Type: Journal    
DOI: 10.1017/S0269888912000331     Document Type: Review

References (133)

1. 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.
2. 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.
3. 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. (Pubitemid 33651997)
4. Alm, E. & Arkin, A. P. 2003. Biological Networks. Current Opinion in Structural Biology 13(2), 193-202. (Pubitemid 36515147)
5. 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.
6. 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.
7. Bayardo, R. J. Jr 1998. Efficiently Mining Long Patterns from Databases. In Proceedings of the 1998 International Conference on Management of Data, 85-93. (Pubitemid 128655959)
8. 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.
9. Borgelt, C. & Berthold, M. 2002. Mining molecular fragments: finding relevant substructures of molecules. In Proceedings of International Conference on Data Mining, 211-218.
10. Borgwardt, K. M. & Kriegel, H. P. 2005. Shortest-path kernels on graphs. In Proceedings of the 2005 International Conference on Data Mining, 74-81. (Pubitemid 47385679)
11. 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.
12. Bunke, H. & Allerman, G. 1983. Inexact graph matching for structural pattern recognition. Pattern Recognition Letters 1(4), 245-253.
13. 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.
14. 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.
15. 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. (Pubitemid 126774144)
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. 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. (Pubitemid 38824884)
23. 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. (Pubitemid 40951991)
24. 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.
25. Chung, J. C. 1987. O(n2.5) time algorithm for subgraph homeomorphism problem on trees. Journal of Algorithms 8, 106-112.
26. 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.
27. Cook, D. J. & Holder, L. B. 1994. Substructure discovery using minimum description length and background knowledge. Journal of Artificial Intelligence Research 1, 231-255.
28. Cook, D. J. & Holder, L. B. 2000. Graph-based data mining. IEEE Intelligent Systems 15(2), 32-41. (Pubitemid 30587666)
29. 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.
30. 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.
31. Cui, J. H., Kim, J., Maggiorini, D., Boussetta, K. & Gerla, M. 2005. Aggregated multicast-a comparative study. Cluster Computing 8(1), 15-26.
32. 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. (Pubitemid 41259847)
33. 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.
34. 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.
35. 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. (Pubitemid 40415749)
36. Flake, G., Tarjan, R. & Tsioutsiouliklis, K. 2004. Graph clustering and minimum cut trees. Internet Mathematics 1, 385-408.
37. Fortin, S. 1996. The Graph Isomorphism Problem Technical report, no. TR06-20, The University of Alberta.
38. 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.
39. Garey, M. R. & Johnson, D. S. 1979. Computers and intractability-a guide to the theory of NP-completeness. W.H. Freeman and Company.
40. Gä rtner, 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. (Pubitemid 37053200)
41. Getoor, L. & Diehl, C. 2005. Link mining: a survey. ACM SIGKDD Explorations Newsletter 7(2), 3-12.
42. Gibbons, A. 1985. Algorithmic Graph Theory. Cambridge University Press.
43. 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.
44. 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.
45. 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.
46. Han, J. & Kamber, M. 2006. Data Mining Concepts and Techniques, 2nd edition. Morgan Kaufmann.
47. 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.
48. 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. (Pubitemid 47142811)
49. Harary, F. & Ross, I. C. 1957. A procedure for clique detection using the group matrix. Sociometry 20(3), 205-215.
50. Hein, J., Jiang, T., Wang, L. & Zhang, K. 1996. On the complexity of comparing evolutionary trees. Discrete Applied Mathematics 71(1-3), 153-169. (Pubitemid 126391777)
51. 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. (Pubitemid 47385690)
52. 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.
53. 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.
54. 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.
55. 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. (Pubitemid 40114974)
56. Huang, X. & Lai, W. 2006. Clustering graphs for visualization via node similarities. Visual Language and Computing 17, 225-253. (Pubitemid 43632410)
57. 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.
58. 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.
59. 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.
60. 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.
61. 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.
62. 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.
63. 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.
64. 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. (Pubitemid 37205946)
65. Kleinberg, J. M. 1998. Authoritative sources in a hyper-linked environment. In Proceedings of ACM-SIAM Symposium Discrete Algorithms, 668-677.
66. Kosala, R. & Blockeel, H. 2000. Web mining research: a survey. ACM SIGKDD Explorations Newsletter 2(1), 1-15.
67. Koyutü rk, M., Grama, A. & Szpankowski, W. 2004. An efficient algorithm for detecting frequent subgraphs in biological networks. Journal of Bioinformatics 20(1), 200-207.
68. 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.
69. Kuramochi, M. & Karypis, G. 2001. Frequent subgraph discovery. In Proceedings of the International Conference on Data Mining, 313-320.
70. Kuramochi, M. & Karypis, G. 2002. Discovering frequent geometric subgraphs. In Proceedings of the IEEE International Conference on Data Mining, 258-265.
71. Kuramochi, M. & Karypis, G. 2004a. An efficient algorithm for discovering frequent subgraphs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1038-1051.
72. 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. (Pubitemid 40731077)
73. 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.
74. Kuramochi, M. & Karypis, G. 2005. Finding frequent patterns in a large sparse graph. Data Mining and Knowledge Discovery 11(3), 243-271. (Pubitemid 41669194)
75. Liu, B. 2008. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer.
76. Liu, T. L. & Geiger, D. 1999. Approximate tree matching and shape similarity. In Proceedings of 7th International Conference on Computer Vision, 456-462. (Pubitemid 32080076)
77. Matula, D. W. 1978. Subtree isomorphism in O(n5/2) Annals of Discrete Mathematics 2, 91-106.
78. McKay, B. D. 1981. Practical graph isomorphism. Congressus Numerantium 30, 45-87.
79. 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. (Pubitemid 128741329)
80. 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. (Pubitemid 35231534)
81. 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.
82. Newman, M. E. J. 2004. Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems 38(2), 321-330.
83. 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.
84. 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. (Pubitemid 40114985)
85. 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. (Pubitemid 351776323)
86. Paul, S. 1998. Multicasting on the Internet and Its Applications. Kluwer Academic Publishers.
87. 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. (Pubitemid 43218284)
88. 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. (Pubitemid 32457120)
89. Preiss, B. R. 1998. Data Structures and Algorithms with Object-Oriented Design Patterns in C11. Wiley.
90. Prü fer, H. 1918. Neuer beweis eines satzes ü ber permutationen. Archiv für Mathematik und Physik 27, 742-744.
91. Read, R. C. & Corneil, D. G. 1977. The graph isomorph disease. Journal of Graph Theory 1, 339-363.
92. Rü ckert, 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.
93. 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.
94. Schreiber, F. & Schwö bbermeyer, H. 2005. Frequency concepts and pattern detection for the analysis of motifs in networks. Transactions on Computational Systems Biology 3, 89-104. (Pubitemid 350259106)
95. Shamir, R. & Tsur, D. 1999. Faster subtree isomorphism. Journal of Algorithms 33(2), 267-280. (Pubitemid 129607236)
96. Shapiro, L. G. & Haralick, R. M. 1981. Structural descriptions and inexact matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 3, 504-519.
97. 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. (Pubitemid 35009480)
98. 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.
99. 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. (Pubitemid 40261997)
100. 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.
101. 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. (Pubitemid 44019445)
102. 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. (Pubitemid 47203591)
103. 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.
104. 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.
105. Ullmann, J. R. 1976. An algorithm for subgraph isomorphism. Journal of the ACM 23(1), 31-42.
106. Valiente, G. 2002. Algorithms on Trees and Graphs. Springer.
107. Vanetik, N. 2002. Discovery of Frequent Patterns in Semi-structured Data. Department of Computer Science, Ben Gurion University.
108. 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.
109. Vanetik, N., Shimony, S. E. & Gudes, E. 2006. Support measures for graph data. Journal of Data Mining and Knowledge Discovery 13(2), 243-260. (Pubitemid 44352884)
110. 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. (Pubitemid 38824929)
111. 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. (Pubitemid 40114941)
112. 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. (Pubitemid 44535593)
113. Washo, T. & Motoda, H. 2003. State of the art of graph-based data mining. SIGKDD Explorations 5, 59-68.
114. West, D. B. 2000. Introduction to Graph Theory, 2nd edition. Prentice Hall.
115. Wö rlein, 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.
116. 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. (Pubitemid 44535541)
117. Yan, X. & Han, J. W. 2002. gSpan: graph-based substructure pattern mining. In Proceedings of the International Conference on Data Mining, 721-724.
118. 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.
119. 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.
120. 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. (Pubitemid 43218294)
121. 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. (Pubitemid 43038974)
122. 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.
123. 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.
124. Zaki,M. J. 2002. EfficientlyMining Frequent Trees in a Forest. In Proceedings of the SIGKDD 2002. ACM, 71-80.
125. Zaki, M. J. 2005a. Efficiently mining frequent embedded unordered trees. Fundamenta Informaticae 66(1-2), 33-52. (Pubitemid 41218091)
126. 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. (Pubitemid 41259846)
127. 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.
128. 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. (Pubitemid 44535593)
129. 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. (Pubitemid 43955540)
130. 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.
131. 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.
132. 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.
133. 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.