Skip to main content
SpringerLink
Log in

Similarity Search in Large-Scale Graph Databases

  • Chapter
  • First Online:
Book cover Handbook of Big Data Technologies

Abstract

Graphs are ubiquitous and play an essential role in modeling and representing complex structures in real-world networked applications. Given a graph database that comprises a large collection of graphs, it is fundamental and critical to enable fast and flexible search for structurally similar graphs. In this paper, we survey recent graph similarity search techniques and specifically focus on the work based on the graph edit distance (GED) metric. State-of-the-art approaches for the GED based similarity search typically adopt a pruning and verification framework. They first take advantage of some easy-to-compute lower-bounds of graph edit distance, and use novel graph indexing structures to efficiently evaluate such lower-bounds between graphs in the graph database and the query graph. This way, graphs that violate the GED lower-bound constraints can be identified and filtered from the graph database from further investigation. Then, the costly GED verification is performed only for the graphs that pass the GED lower-bound evaluation. We examine existing GED lower-bounds, graph index structures, and similarity search algorithms in detail, and compare different similarity search methods from multiple aspects including index construction cost, similarity search performance, and applicability in real-world graph databases. In the end, we envision and discuss the future research directions related to similarity search and high-performance query processing in large-scale graph databases.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 349.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 449.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 449.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. C.C. Aggarwal, H. Wang, Managing and Mining Graph Data (Springer, US, 2010)

    Google Scholar 

  2. L. Babai, Graph isomorphism in quasipolynomial time. in Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing (STOC’16) (2016), pp. 684–697

    Google Scholar 

  3. D.F. Barbieri, D. Braga, S. Ceri, E.D. Valle, M. Grossniklaus, Querying rdf streams with c-sparql. SIGMOD Rec. 39(1), 20–26 (2010)

    Article  MATH  Google Scholar 

  4. P. Barceló Baeza, Querying graph databases. in Proceedings of the 32nd Symposium on Principles of Database Systems (PODS’13) (2013), pp. 175–188

    Google Scholar 

  5. H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne, The protein data bank. Nucleic Acids Res. 28, 235–242 (2000)

    Article  Google Scholar 

  6. S. Berretti, A. Del Bimbo, E. Vicario, Efficient matching and indexing of graph models in content-based retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1089–1105 (2001)

    Article  Google Scholar 

  7. K.M. Borgwardt, H.-P. Kriegel, Shortest-path kernels on graphs. in Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM’05) (2005), pp. 74–81

    Google Scholar 

  8. H. Bunke, On a relation between graph edit distance and maximum common subgraph. Pattern Recogn. Lett. 18(9), 689–694 (1997)

    Article  Google Scholar 

  9. H. Bunke, Error correcting graph matching: on the influence of the underlying cost function. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 917–922 (1999)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  11. X. Chen, K.S. Candan, M.L. Sapino, P.Shakarian, KSGM: Keynode-driven scalable graph matching. in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM’15) (2015), pp. 1101–1110

    Google Scholar 

  12. H. Cheng, D. Lo, Y. Zhou, X. Wang, X. Yan, Identifying bug signatures using discriminative graph mining. in Proceedings of the Eighteenth International Symposium on Software Testing and Analysis (ISSTA’09) (2009), pp. 141–152

    Google Scholar 

  13. J. Cheng, Y. Ke, W. Ng, Efficient query processing on graph databases. ACM Trans. Database Syst. 34(1), 2:1–2:48 (2009)

    Article  Google Scholar 

  14. S. Choudhury, L. Holder, G. Chin, A. Ray, S. Beus, J. Feo, Streamworks: a system for dynamic graph search. in Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data (SIGMOD’13) (2013), pp. 1101–1104

    Google Scholar 

  15. D. Conte, P. Foggia, C. Sansone, M. Vento, Thirty years of graph matching in pattern recognition. Int. J. Pattern Recognit. Artif. Intell. 18(3), 265–298 (2004)

    Article  Google Scholar 

  16. D.J. Cook, L.B. Holder, Mining Graph Data (Wiley, New Jersey, 2006)

    Book  MATH  Google Scholar 

  17. R. Fagin, A. Lotem, M. Naor, Optimal aggregation algorithms for middleware. in Proceedings of the Twentieth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS’01) (2001), pp. 102–113

    Google Scholar 

  18. W. Fan, J. Li, S. Ma, N. Tang, Y. Wu, Y. Wu, Graph pattern matching: from intractable to polynomial time. Proc. VLDB Endow. 3(1–2), 264–275 (2010)

    Article  Google Scholar 

  19. S. Fankhauser, K. Riesen, H. Bunke, Speeding up graph edit distance computation through fast bipartite matching. in Proceedings of the 8th International Conference on Graph-based Representations in Pattern Recognition (GBRPR’11) (2011), pp. 102–111

    Google Scholar 

  20. B. Gallagher, Matching structure and semantics: a survey on graph-based pattern matching. in American Association for Artificial Intelligence (AAAI’06), vol. 6 (2006), pp. 45–53

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  22. M.R. Garey, D.S. Johnson, Computers and Intractability; A Guide to the Theory of NP-Completeness (W. H. Freeman & Co., New York, 1990)

    MATH  Google Scholar 

  23. K. Gouda, M. Arafa, An improved global lower bound for graph edit similarity search. Pattern Recogn. Lett. 58, 8–14 (2015)

    Article  Google Scholar 

  24. L. Gravano, P.G. Ipeirotis, H.V. Jagadish, N. Koudas, S. Muthukrishnan, D. Srivastava, Approximate string joins in a database (almost) for free. in Proceedings of the 27th International Conference on Very Large Data Bases (VLDB’01) (2001), pp. 491–500

    Google Scholar 

  25. W.-S. Han, J. Lee, J.-H. Lee, Turboiso: towards ultrafast and robust subgraph isomorphism search in large graph databases. in Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data (SIGMOD’13) (2013), pp. 337–348

    Google Scholar 

  26. W.-S. Han, M.-D. Pham, J. Lee, R. Kasperovics, J.X. Yu, Igraph in action: performance analysis of disk-based graph indexing techniques. in Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data (SIGMOD’11) (2011), pp. 1241–1242

    Google Scholar 

  27. H. He, A.K. Singh, Closure-tree: an index structure for graph queries. in Proceedings of the 22nd International Conference on Data Engineering (ICDE’06) (2006), pp. 38–49

    Google Scholar 

  28. H. He, A.K. Singh, Graphs-at-a-time: query language and access methods for graph databases. in Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data (SIGMOD’08) (2008), pp. 405–418

    Google Scholar 

  29. H.H. Hung, S.S. Bhowmick, B.Q. Truong, B. Choi, S. Zhou, Quble: blending visual subgraph query formulation with query processing on large networks. in Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data (SIGMOD’13) (2013), pp. 1097–1100

    Google Scholar 

  30. N. Jayaram, S. Goyal, C. Li, VIIQ: Auto-suggestion enabled visual interface for interactive graph query formulation. Proc. VLDB Endow. 8(12), 1940–1943 (2015)

    Article  Google Scholar 

  31. C. Jin, S.S. Bhowmick, X. Xiao, J. Cheng, B. Choi, GBLENDER: towards blending visual query formulation and query processing in graph databases. in Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data (SIGMOD’10) (2010), pp. 111–122

    Google Scholar 

  32. A. Khan, N. Li, X. Yan, Z. Guan, S. Chakraborty, S. Tao, Neighborhood based fast graph search in large networks. in Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data (SIGMOD’11) (2011), pp. 901–912

    Google Scholar 

  33. A. Khan, Y. Wu, C.C. Aggarwal, X. Yan, NeMa: Fast graph search with label similarity. Proc. VLDB Endow. 6(3), 181–192 (2013)

    Article  Google Scholar 

  34. H.W. Kuhn, B. Yaw, The hungarian method for the assignment problem. Naval Res. Logist. Quart. 83–97 (1955)

    Google Scholar 

  35. J. Lee, W.-S. Han, R. Kasperovics, J.-H. Lee, An in-depth comparison of subgraph isomorphism algorithms in graph databases. in Proceedings of the 39th International Conference on Very Large Data Bases (PVLDB’13) (2013), pp. 133–144

    Google Scholar 

  36. C. Li, J. Lu, Y. Lu, Efficient merging and filtering algorithms for approximate string searches. in Proceedings of the 2008 IEEE 24th International Conference on Data Engineering (ICDE’08) (2008), pp. 257–266

    Google Scholar 

  37. C. Li, B. Wang, X. Yang, VGRAM: improving performance of approximate queries on string collections using variable-length grams. in Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB’07) (2007), pp. 303–314

    Google Scholar 

  38. S. Ma, Y. Cao, W. Fan, J. Huai, T. Wo, Strong simulation: Capturing topology in graph pattern matching. ACM Trans. Database Syst. 39(1), 4:1–4:46 (2014)

    Google Scholar 

  39. M. Neuhaus, H. Bunke, Bridging the Gap Between Graph Edit Distance and Kernel Machines (World Scientific Publishing, Singapore, 2007)

    Book  MATH  Google Scholar 

  40. H. Ogata, S. Goto, K. Sato, W. Fujibuchi, H. Bono, M. Kanehisa, KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 27(1), 29–34 (1999)

    Article  Google Scholar 

  41. J. Qin, W. Wang, Y. Lu, C. Xiao, X. Lin, Efficient exact edit similarity query processing with the asymmetric signature scheme. in Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data (SIGMOD’11) (2011), pp. 1033–1044

    Google Scholar 

  42. S.A. Rahman, M. Bashton, G.L. Holliday, R. Schrader, J.M. Thornton, Small molecule subgraph detector (SMSD) toolkit. J. Cheminform. 1, 1–12 (2009)

    Article  Google Scholar 

  43. S. Ranu, M. Hoang, A. Singh, Answering top-k representative queries on graph databases. in Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (SIGMOD’14) (2014), pp. 1163–1174

    Google Scholar 

  44. S. Ranu, A.K. Singh, Indexing and mining topological patterns for drug discovery. in Proceedings of the 15th International Conference on Extending Database Technology (EDBT’12) (2012), pp. 562–565

    Google Scholar 

  45. K. Riesen, S. Emmenegger, H. Bunke, A novel software toolkit for graph edit distance computation. in 9th International Workshop on Graph-Based Representations in Pattern Recognition (2013), pp. 142–151

    Google Scholar 

  46. S. Sakr, S. Elnikety, Y. He, G-SPARQL: A hybrid engine for querying large attributed graphs. in Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM’12) (2012), pp. 335–344

    Google Scholar 

  47. M. Schmidt, M. Meier, G. Lausen, Foundations of SPARQL query optimization. in Proceedings of the 13th International Conference on Database Theory (ICDT’10) (2010), pp. 4–33

    Google Scholar 

  48. H. Shang, X. Lin, Y. Zhang, J.X. Yu, W. Wang, Connected substructure similarity search. in Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data (SIGMOD’10) (2010), pp. 903–914

    Google Scholar 

  49. A. Tefas, C. Kotropoulos, I. Pitas, Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Trans. Pattern Anal. Mach. Intell. 23(7), 735–746 (2001)

    Article  Google Scholar 

  50. Y. Tian, R.C. Mceachin, C. Santos, D.J. States, J.M. Patel, SAGA: a subgraph matching tool for biological graphs. Bioinformatics 23(2), 232–239 (2007)

    Article  Google Scholar 

  51. E. Ukkonen, Approximate string-matching with q-grams and maximal matches. Theor. Comput. Sci. 92(1), 191–211 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  52. J.R. Ullmann, An algorithm for subgraph isomorphism. J. ACM 23(1), 31–42 (1976)

    Article  MathSciNet  Google Scholar 

  53. G. Wang, B. Wang, X. Yang, G. Yu, Efficiently indexing large sparse graphs for similarity search. IEEE Trans. Knowl. Data Eng. 24(3), 440–451 (2012)

    Article  Google Scholar 

  54. X. Wang, X. Ding, A.K.H. Tung, S. Ying, H. Jin, An efficient graph indexing method. in Proceedings of the 2012 IEEE 28th International Conference on Data Engineering (ICDE’12) (2012), pp. 210–221

    Google Scholar 

  55. X. Yan, J. Han, gSpan: graph-based substructure pattern mining. in Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM’02) (2002), pp. 721–724

    Google Scholar 

  56. X. Yan, P.S. Yu, J. Han, Graph indexing: a frequent structure-based approach. in Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data (SIGMOD’04) (2004), pp. 335–346

    Google Scholar 

  57. X. Yan, P.S. Yu, J. Han, Substructure similarity search in graph databases. in Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data (SIGMOD’05) (2005), pp. 766–777

    Google Scholar 

  58. Y. Yuan, G. Wang, J.Y. Xu, L. Chen, Efficient distributed subgraph similarity matching. VLDB J. 24(3), 369–394 (2015)

    Article  Google Scholar 

  59. Z. Zeng, A.K.H. Tung, J. Wang, J. Feng, L. Zhou, Comparing stars: On approximating graph edit distance. Proc. VLDB Endow. 2(1), 25–36 (2009)

    Article  Google Scholar 

  60. S. Zhang, J. Yang, W. Jin, SAPPER: Subgraph indexing and approximate matching in large graphs. Proc. VLDB Endow. 3(1–2), 1185–1194 (2010)

    Article  Google Scholar 

  61. Z. Zhang, M. Hadjieleftheriou, B.C. Ooi, D. Srivastava, Bed-tree: an all-purpose index structure for string similarity search based on edit distance. in Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data (SIGMOD’10) (2010), pp. 915–926

    Google Scholar 

  62. P. Zhao, J. Han, On graph query optimization in large networks. Proc. VLDB Endow. 3(1–2), 340–351 (2010)

    Article  Google Scholar 

  63. P. Zhao, J.X. Yu, P.S. Yu, Graph indexing: tree + delta \(\ge \) graph. in Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB’07) (2007), pp. 938–949

    Google Scholar 

  64. X. Zhao, C. Xiao, X. Lin, Q. Liu, W. Zhang, A partition-based approach to structure similarity search. PVLDB 7(3), 169–180 (2013)

    Google Scholar 

  65. X. Zhao, C. Xiao, X. Lin, W. Wang, Efficient graph similarity joins with edit distance constraints. in Proceedings of the 2012 IEEE 28th International Conference on Data Engineering (ICDE’12) (2012), pp. 834–845

    Google Scholar 

  66. X. Zhao, C. Xiao, X. Lin, W. Wang, Y. Ishikawa, Efficient processing of graph similarity queries with edit distance constraints. VLDB J. 22(6), 727–752 (2013)

    Article  Google Scholar 

  67. W. Zheng, L. Zou, X. Lian, D. Wang, D. Zhao, Graph similarity search with edit distance constraint in large graph databases. in Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management (CIKM’13) (2013), pp. 1595–1600

    Google Scholar 

  68. G. Zhu, X. Lin, K. Zhu, W. Zhang, J.X. Yu, TreeSpan: efficiently computing similarity all-matching. in Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (SIGMOD’12) (2012), pp. 529–540

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Florida State University, 1017 Academic Way, James Love Building, Tallahassee, FL, 32306, USA

    Peixiang Zhao

Authors
  1. Peixiang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peixiang Zhao .

Editor information

Editors and Affiliations

  1. School of Information Technologies, The University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

  2. The School of Computer Science, The University of New South Wales, Eveleigh, New South Wales, Australia

    Sherif Sakr

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Zhao, P. (2017). Similarity Search in Large-Scale Graph Databases. In: Zomaya, A., Sakr, S. (eds) Handbook of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-49340-4_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49340-4_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49339-8

  • Online ISBN: 978-3-319-49340-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation