article

Inexact subgraph isomorphism in MapReduce

Author:

Todd PlantengaAuthors Info & Claims

Journal of Parallel and Distributed Computing, Volume 73, Issue 2

Pages 164 - 175

https://doi.org/10.1016/j.jpdc.2012.10.005

Published: 01 February 2013 Publication History

Abstract

Inexact subgraph matching based on type-isomorphism was introduced by Berry et al. [J. Berry, B. Hendrickson, S. Kahan, P. Konecny, Software and algorithms for graph queries on multithreaded architectures, in: Proc. IEEE International Parallel and Distributed Computing Symposium, IEEE, 2007, pp. 1-14] as a generalization of the exact subgraph matching problem. Enumerating small subgraph patterns in very large graphs is a core problem in the analysis of social networks, bioinformatics data sets, and other applications. This paper describes a MapReduce algorithm for subgraph type-isomorphism matching. The MapReduce computing framework is designed for distributed computing on massive data sets, and the new algorithm leverages MapReduce techniques to enable processing of graphs with billions of vertices. The paper also introduces a new class of walk-level constraints for narrowing the set of matches. Constraints meeting criteria defined in the paper are useful for specifying more precise patterns and for improving algorithm performance. Results are provided on a variety of graphs, with size ranging up to billions of vertices and edges, including graphs that follow a power law degree distribution.

References

[1]

Apache Hadoop, Apache Hadoop project, 2011. http://hadoop.apache.org/.

[2]

J. Berry, Practical heuristics for inexact subgraph isomorphism, Technical Report SAND2011-6558W, Sandia National Laboratories, Albuquerque, NM, 2011.

[3]

Berry, J., Hendrickson, B., Kahan, S. and Konecny, P., Software and algorithms for graph queries on multithreaded architectures. In: Proc. IEEE International Parallel and Distributed Computing Symposium, IEEE. pp. 1-14.

[4]

J.W. Berry, D.J. Nordman, C.A. Phillips, A.G. Wilson, Listing triangles in expected linear time on a class of power law graphs, Technical Report SAND2010-4474C, Sandia National Laboratories, Albuquerque, NM, 2010.

[5]

M. Bröcheler, A. Publiese, V. Subrahamian, DOGMA: a disk-oriented graph matching algorithm for RDF databases, in: 8th International Semantic Web Conference, ISWC 2009, 2009, pp. 97-113.

[6]

M. Bröcheler, A. Publiese, V. Subrahamian, COSI: cloud oriented subgraph identification in massive social networks, in: 2010 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2010, 2010, pp. 248-255.

[7]

Bunke, H. and Allermann, G., Inexact graph matching for structural pattern recognition. Pattern Recognition Letters. 245-253.

[8]

Chakrabarti, D., Faloutsos, C. and Zhan, Y., R-MAT: a recursive model for graph mining. In: SIAM International Conference on Data Mining, SIAM.

[9]

Clueweb Data, ClueWeb09, 2009. http://boston.lti.cs.cmu.edu/clueweb09.

[10]

Coffman, T., Greenblatt, S. and Marcus, S., Graph-based technologies for intelligence analysis. Communications of the ACM. v47. 45-47.

[11]

Cohen, J., Graph twiddling in a MapReduce world. IEEE Computing in Science & Engineering. 29-41.

[12]

Cook, D.J. and Holder, L.B., Substructure discovery using minimum description length and background knowledge. Journal of Artificial Intelligence Research. v1. 231-255.

[13]

Graph-based data mining. IEEE Intelligent Systems. v15. 32-41.

[14]

Dean, J. and Ghemawat, S., MapReduce: simplified data processing on large clusters. In: OSDI'04: Sixth Symposium on Operating Systems Design and Implementation, USENIX Association.

[15]

Garey, M.R. and Johnson, D.S., Computers and Intractability: A Guide to the Theory of NP-Completeness. 1979. W.H. Freeman & Co.

[16]

Graph 500 Steering Committee, Graph 500 benchmark, 2010. http://www.graph500.org/.

[17]

Han, J. and Kamber, M., Data Mining Concepts and Techniques Second Edition. 2006. Morgan Kaufmann Publishers.

[18]

Integer Hash, Integer hash function, 2011. http://www.concentric.net/~ttwang/tech/inthash.htm.

[19]

Kuramochi, M. and Karypis, G., Finding frequent patterns in a large sparse graph. Data Mining and Knowledge Discovery. v11. 243-271.

[20]

Lemur Toolkit, Lemur toolkit, 2011. http://lemurproject.org/lemur.php.

[21]

Leskovec, J., Chakrabarti, D., Kleinberg, J. and Faloutsos, C., Realistic, mathematically tractable graph generation and evolution, using Kronecker multiplication. In: Knowledge Discovery in Databases: PKDD 2005, vol. 3721. Springer, Berlin, Heidelberg. pp. 133-145.

[22]

Leskovec, J., Chakrabarti, D., Kleinberg, J., Faloutsos, C. and Ghahramani, Z., Kronecker graphs: an approach to modeling networks. Journal of Machine Learning Research. v11. 985-1042.

[23]

Liu, Y., Jiang, X., Chen, H., Ma, J. and Zhang, X., MapReduce-based pattern finding algorithm applied in Motif detection for prescription compatibility network. In: Joller, Josef, Dou, Yong, Gruber, Ralf (Eds.), Lecture Notes in Computer Science, vol. 5737. pp. 341-355.

[24]

Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P. and Bhattacharjee, B., Measurement and analysis of online social networks. In: Proceedings of the 5th ACM/Usenix Internet Measurement Conference, ACM.

[25]

Nilsson, N., Principles of Artificial Intelligence. 1980. Tioga Publishing Co.

[26]

Plimpton, S.J. and Devine, K.D., MapReduce in MPI for large-scale graph algorithms. Parallel Computing. v37. 610-632.

[27]

D. Sasha, J.T. Want, R. Guigno, Algorithmics and applications of tree and graph searching, in: Proceedings of the 21st ACM PODS, 2002, pp. 39-52.

[28]

C. Seshadhri, A. Pinar, T.G. Kolda, An in-depth analysis of stochastic Kronecker graphs, in: ICDM 2011: Proceedings of the 2011 IEEE International Conference on Data Mining, 2011, pp. 587-596.

[29]

Tong, H., Gallagher, B., Faloutsos, C. and Eliassi-Rad, T., Fast best-effort pattern matching in large attributed graphs. In: Proc. 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM. pp. 737-746.

[30]

Ullmann, J., An algorithm for subgraph isomorphism. Journal of the ACM. v23. 31-42.

[31]

White, T., Hadoop: The Definitive Guide. 2010. second ed. O'Reilly Media, Yahoo Press.

[32]

Z. Zhao, G. Wang, A.R. Butt, M. Khan, V.A. Kumar, M.V. Marathe, SAHad: subgraph analysis in massive networks using Hadoop, in: Proceedings of the 26th IEEE International Parallel and Distributed Processing Symposium, 2012, pp. 390-401.

Digital Library

Cited By

Sharma AMehta DWu B(2024)Understanding High-Performance Subgraph Pattern Matching: A Systems PerspectiveProceedings of the 7th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)10.1145/3661304.3661897(1-12)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3661304.3661897
Li FZou ZLi J(2023)Durable Subgraph Matching on Temporal GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.314899535:5(4713-4726)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3148995
Yang ZLai LLin XHao KZhang WLi GLi ZIdreos SSrivastava D(2021)HUGE: An Efficient and Scalable Subgraph Enumeration SystemProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457237(2049-2062)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457237
Show More Cited By

Inexact subgraph isomorphism in MapReduce
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

Recommendations

Polynomial-time algorithms for Subgraph Isomorphism in small graph classes of perfect graphs

Given two graphs, Subgraph Isomorphism is the problem of deciding whether the first graph (the base graph) contains a subgraph isomorphic to the second one (the pattern graph). This problem is NP-complete even for very restricted graph classes such as ...
Parallel Planar Subgraph Isomorphism and Vertex Connectivity
SPAA '20: Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures

We present the first parallel fixed-parameter algorithm for subgraph isomorphism in planar graphs, bounded-genus graphs, and, more generally, all minor-closed graphs of locally bounded treewidth. Our randomized low depth algorithm has a near-linear work ...
Subgraph Isomorphism Building on A Hierarchical Query Graph
ICCDA '21: Proceedings of the 2021 5th International Conference on Compute and Data Analysis

Subgraph isomorphism is an essential problem of graph theory. It has broad application on information retrieval in many research field, such as biology, chemistry, knowledge graph and social network. The settlement to graph isomorphism is to find the ...

Comments

Information & Contributors

Information

Published In

cover image Journal of Parallel and Distributed Computing

Journal of Parallel and Distributed Computing Volume 73, Issue 2

February, 2013

171 pages

ISSN:0743-7315

Issue’s Table of Contents

Copyright © Elsevier Inc. © 2012.

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 February 2013

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

21
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 31 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sharma AMehta DWu B(2024)Understanding High-Performance Subgraph Pattern Matching: A Systems PerspectiveProceedings of the 7th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)10.1145/3661304.3661897(1-12)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3661304.3661897
Li FZou ZLi J(2023)Durable Subgraph Matching on Temporal GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.314899535:5(4713-4726)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3148995
Yang ZLai LLin XHao KZhang WLi GLi ZIdreos SSrivastava D(2021)HUGE: An Efficient and Scalable Subgraph Enumeration SystemProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457237(2049-2062)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457237
Bouhenni SYahiaoui SNouali-Taboudjemat NKheddouci H(2021)A Survey on Distributed Graph Pattern Matching in Massive GraphsACM Computing Surveys10.1145/343972454:2(1-35)Online publication date: 9-Feb-2021
https://dl.acm.org/doi/10.1145/3439724
Reza THalawa HRipeanu MSanders GPearce R(2021)Scalable Pattern Matching in Metadata Graphs via Constraint CheckingACM Transactions on Parallel Computing10.1145/34343918:1(1-45)Online publication date: 4-Jan-2021
https://dl.acm.org/doi/10.1145/3434391
Bhattarai BLiu HHuang HBoncz PManegold SAilamaki ADeshpande AKraska T(2019)CECIProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3300086(1447-1462)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3300086
Reza TRipeanu MTripoul NSanders GPearce R(2018)PruneJuiceProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.5555/3291656.3291684(1-17)Online publication date: 11-Nov-2018
https://dl.acm.org/doi/10.5555/3291656.3291684
Lai LQin LLin XChang L(2018)Scalable subgraph enumeration in MapReduceProceedings of the VLDB Endowment10.14778/2794367.27943688:10(974-985)Online publication date: 5-Oct-2018
https://dl.acm.org/doi/10.14778/2794367.2794368
Park HSilvestri FPagh RChung CMyaeng SKang U(2018)Enumerating Trillion Subgraphs On Distributed SystemsACM Transactions on Knowledge Discovery from Data10.1145/323719112:6(1-30)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1145/3237191
Ko SHan WDas GJermaine CBernstein P(2018)TurboGraph++Proceedings of the 2018 International Conference on Management of Data10.1145/3183713.3196915(395-410)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3183713.3196915
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents