Skip to main content
Log in

AGraP: an algorithm for mining frequent patterns in a single graph using inexact matching

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Frequent graph mining algorithms commonly use graph isomorphism to identify occurrences of a given pattern, but in the last years, a few works have focused on the case where a pattern could differ from its occurrences, which can be important to analyze noisy data. These later algorithms allow differences in labels and structural differences in edges, but to the best of our knowledge, none of them considers structural differences in vertices. How can we identify occurrences that differ by one (or several) nodes from the pattern they represent? Our work approaches the problem of frequent graph pattern mining with two main characteristics. First, we use inexact matching, allowing structural differences in both edges and vertices. Second, we focus on the problem of mining patterns in a single graph, a problem that has been less explored than the case in which patterns are mined from a graph collection. In this paper, we introduce two similarity functions to compare graphs using inexact matching and an algorithm, AGraP, able to identify patterns that can have structural differences with respect to their occurrences. Our experimental results show that AGraP is able to find patterns that cannot be found by other state-of-the-art algorithms. Additionally, we show that the patterns mined by AGraP are useful in classification tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Acosta-Mendoza N, Gago-Alonso A, Medina-Pagola JE (2012) Frequent approximate subgraphs as features for graph-based image classification. Knowl-Based Syst 27:381–392

    Article  Google Scholar 

  2. Aggarwal CC, Wang H (2010) Managing and mining graph data. Springer, Berlin

    Book  Google Scholar 

  3. Alguliev R, Aliguliyev R, Ganjaliyev F (2011) Extracting a heterogeneous social network of academic researchers on the web based on information retrieved from multiple sources. Am J Oper Res 2(1):33–38

    Article  Google Scholar 

  4. Anchuri P, Zaki MJ, Barkol O, Bergman R, Felder Y, Golan S, Sityon A (2012) Graph mining for discovering infrastructure patterns in configuration management databases. Knowl Inf Syst 33(3):491–522

    Article  Google Scholar 

  5. Borgelt C, Berthold MR (2002) Mining molecular fragments: finding relevant substructures of molecules. In: Proceedings of the 2002 IEEE international conference on data mining. Maebashi City, Japan, pp 51–58

  6. Bringmann B, Nijssen S (2008) What is frequent in a single graph?. In: Washio T, Suzuki E, Ting K, Inokuchi A (eds) Advances in knowledge discovery and data mining, lecture notes in computer science, Springer, Berlin 5012:858–863

  7. Bunke H, Shearer K (1998) A graph distance metric based on the maximal common subgraph. Pattern Recognit Lett 19(3–4):255–259

    Article  Google Scholar 

  8. Bunke H, Riesen K (2011) Recent advances in graph-based pattern recognition with applications in document analysis. Pattern Recognit 44:1057–1067

    Article  Google Scholar 

  9. Chen C, Yan X, Zhu F, Han J (2007) gApprox: mining frequent approximate patterns from a massive network. In: ICDM, IEEE computer society, pp 445–450

  10. Cook D, Holder L (2007) Mining graph data. Wiley-Interscience, New York

    Google Scholar 

  11. Dehmer M, Emmert-Streib F (2007) Comparing large graphs efficiently by margins of feature vectors. Appl Math Comput 188(2):1699–1710

    Article  Google Scholar 

  12. Deshpande M, Kuramochi M, Wale N, Karypis G (2005) Frequent substructure-based approaches for classifying chemical compounds. IEEE Trans Knowl Data Eng 17(8):1036–1050

    Article  Google Scholar 

  13. Fiedler M, Borgelt C (2007) Support computation for mining frequent subgraphs in a single graph. In: 5th International workshop on mining and learning with graphs, pp 25–30

  14. Gago-Alonso A, Medina-Pagola J, Carrasco-Ochoa J, Martnez-Trinidad J (2008) Mining frequent connected subgraphs reducing the number of candidates. In: Daelemans W, Goethals B, Morik K (eds) Machine learning and knowledge discovery in databases, lecture notes in computer science, Springer, Berlin, 5211:365–376

  15. Gao X, Xiao B, Tao D, Li X (2010) A survey of graph edit distance. Pattern Anal Appl 13(1):113–129

    Article  Google Scholar 

  16. Gärtner T (2002) Exponential an geometric kernels for graphs. In: NIPS*02 workshop on unreal data, principles of modeling nonvectorial data

  17. Gärtner T, Flach P, Wrobel, S (2003) On graph kernels: hardness results and efficient alternatives. In: Conference on learning theory, pp 129–143

  18. Hellal A, Romdhane LB (2013) Nodar: mining globally distributed substructures from a single labeled graph. J Intell Inf Syst 40(1):1–15

    Article  Google Scholar 

  19. Hidovic D, Pelillo M (2004) Metrics for attributed graphs based on the maximal similarity common subgraph. In: IJPRAI, pp 299–313

  20. Holder LB (1988) Substructure discovery in subdue. Technical Report UILU-ENG-88-2220, Department of Computer Science, University of Illinois, Urbana

  21. Holder L, Cook D, Djoko S (1994) Substructure discovery in the SUBDUE system. In: Proceedings of the AAAI workshop on knowledge discovery in databases, pp 169–180

  22. Huan J, Wang W, Bandyopadhyay D, Snoeyink J, Prins J, Tropsha A (2004), Mining spatial motifs from protein structure graphs. In: Proceedings of the 8th annual international conference on research in computational, molecular biology (RECOMB04), pp 308–315

  23. Jia Y, Zhang J, Huan J (2011) An efficient graph-mining method for complicated and noisy data with real-world applications. Knowl Inf Syst 28(2):423–447

    Article  Google Scholar 

  24. Kondor R, Lafferty J (2002) Diffusion kernels on graphs and other discrete input spaces. In: International conference on machine learning (ICML)

  25. Kuramochi M, Karypis G (2005) Finding frequent patterns in a large sparse graph. Data Min Knowl Discov 11(3):243–271

    Article  Google Scholar 

  26. Kuramochi M, Karypis G (2001) Frequent subgraph discovery. In Proceedings of the international conference data mining (ICDM01), pp 313–320

  27. Kuramochi M, Karypis G (2004) GREW—a scalable frequent subgraph discovery algorithm. In: Proceedings of the fourth IEEE international conference on data mining, pp 439–442

  28. Leskovec J (2009) Stanford large network dataset collection. http://snap.stanford.edu/data/

  29. Lin ZJ, Lyu MR, King I (2012) Matchsim: a novel similarity measure based on maximum neighborhood matching. Knowl Inf Syst 32(1):141–166

    Article  Google Scholar 

  30. López-Presa JL (2009) Efficient algorithms for graph isomorphism testing. PhD Thesis at the Universidad Rey Juan Carlos, Madrid, España

  31. Lu W, Janssen J, Milios E, Japkowicz N, Zhang Y (2007) Node similarity in the citation graph. Knowl Inf Syst 11(1):105–129

    Article  Google Scholar 

  32. Moody J (2001) Peer influence groups: identifying dense clusters in large networks. Soc Netw 23:261–283

    Article  Google Scholar 

  33. National Center for Biotechnology Information. PubChem Database (2004). http://pubchem.ncbi.nlm.nih.gov (Retrieved Nov. 7, 2013)

  34. Nijssen S, Kok JN (2004) A quickstart in frequent structure mining can make a difference. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, pp 647–652

  35. Ramon J, Gärtner T (2003) Expressivity versus efficiency of graph kernels. In: Proceedings of the first international workshop on mining graphs, trees and sequences, pp 65–74

  36. Ranu S, Singh AK (2009) GraphSig: a scalable approach to mining significant subgraphs in large graph databases. In: IEEE 25th international conference on data, engineering, pp 844–855

  37. Riesen K, Bunke, H (2008) IAM graph database repository for graph based pattern recognition and machine learning. In: Proceedings of the international workshop on structural syntatctic and statistical pattern recognition, lecture notes in computer science pp 287–297

  38. Saeedy ME, Kalnis P (2011) GraMi: generalized frequent pattern mining in a single large graph. Technical Report at the Division of Mathematical and Computer Sciences and Engineering, King Abdullah University of Science and Technology

  39. Sanfeliu A, Fu KS (1983) A distance measure between attributed relational graphs for pattern recognition. IEEE Trans Syst Man Cybern 13(3):353–362

    Article  Google Scholar 

  40. Shervashidze N, Vishwanathan SVN, Petri T, Mehlhorn K, Borgwardt K (2009) Efficient graphlet kernels for large graph comparison. In: Proceedings of international conference on artificial intelligence and statistics

  41. Smola AJ, Kondor IR (2003) Kernels and regularization on graphs. In: Schölkopf B, Warmuth MK (eds) Proceedings of the annual conference computational learning theory, pp 144–158

  42. Tan PN, Steinbach M, Kumar V (2006) Introduction to data mining. Pearson International Edition, Pearson Addison Wesley

  43. Vishwanathan SVN, Borgwardt K, Kondor I, Schraudolph N (2008) Graph kernels. J Mach Learn Res 9:1–41

    Google Scholar 

  44. Wale N, Watson IA, Karypis G (2008) Comparison of descriptor spaces for chemical compound retrieval and classification. Knowl Inf Syst 14(3):347–375

    Article  Google Scholar 

  45. Xiao Y, Dong H, Wu W, Xiong M, Wang W, Shi B (2008) Structure-based graph distance measures of high degree of precision. Pattern Recognit 41(12):3547–3561

    Article  Google Scholar 

  46. Yan X, Han J (2002) gSpan: graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE international conference on data mining (ICDM ’02)

  47. Yan X, Yu PS, Han J (2004) Graph indexing: a frequent structure-based approach. In: Proceedings of the SIGMOD conference, pp 335–346

  48. Zhang S, Yang J, Cheedella V (2007) Monkey: approximate graph mining based on spanning trees. In: Proceedings of the IEEE 23rd international conference on data engineering (ICDE 2007), pp 1247–1249

  49. Zou Z, Li J, Gao H, Zhang S (2009) Frequent subgraph pattern mining on uncertain graph data. In: Proceedings of the 18th ACM conference on information and knowledge management (CIKM ’09), pp 583–592

Download references

Acknowledgments

This work was partly supported by the National Council of Science and Technology of Mexico (CONACyT) through the project grants CB2008-106443 and CB2008-106366; and the scholarship grant 256879.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marisol Flores-Garrido.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Flores-Garrido, M., Carrasco-Ochoa, JA. & Martínez-Trinidad, J.F. AGraP: an algorithm for mining frequent patterns in a single graph using inexact matching. Knowl Inf Syst 44, 385–406 (2015). https://doi.org/10.1007/s10115-014-0747-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-014-0747-x

Keywords

Navigation