SAHAD: Subgraph analysis in massive networks using Hadoop

Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium, IPDPS 2012

Volumn , Issue , 2012, Pages 390-401

  Zhao, Zhao ^a   Wang, Guanying ^b   Butt, Ali R ^b   Khan, Maleq ^a   Kumar, V S Anil ^a   Marathe, Madhav V ^a  

^a VIRGINIA BIOINFORMATICS INSTITUTE   (United States)

^b Virginia Polytechnic Institute and State University   (United States)

Author keywords

frequent subgraph; graphlet frequency distribution; Hadoop; MapReduce; motif; subgraph isomorphism

Indexed keywords

FREQUENCY DISTRIBUTIONS; FREQUENT SUBGRAPHS; HADOOP; MAP-REDUCE; MOTIF; SUBGRAPH ISOMORPHISM;

ALGORITHMS; DISTRIBUTED PARAMETER NETWORKS; SET THEORY;

TREES (MATHEMATICS);

EID: 84866851845     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IPDPS.2012.44     Document Type: Conference Paper

References (32)

1. N. Alon, P. Dao, I. Hajirasouliha, F. Hormozdiari, and S. Sahinalp. Biomolecular network motif counting and discovery by color coding. Bioinformatics, 24(13):i241, 2008.
2. N. Alon, R. Yuster, and U. Zwick. Color-coding. Journal of the ACM (JACM), 42(4):856, 1995.
3. Amazon. Elastic Computing Cloud (EC2). http://aws.amazon.com/ec2.
4. C. Barrett, R. Beckman, M. Khan, V. Kumar, M. Marathe, P. Stretz, T. Dutta, and B. Lewis. Generation and analysis of large synthetic social contact networks. In Winter Simulation Conference, 2009.
5. E. Bloedorn, N. J. Rothleder, D. DeBarr, and L. Rosen. Relational graph analysis with real-world constraints: An application in irs tax fraud detection. In AAAI, 2005.
6. C. Borgelt and M. R. Berhold. Mining molecular fragments: Finding relevant substructures of molecules. In ICDM, 2002.
7. M. Bröcheler, A. Pugliese, and V. Subrahmanian. Cosi: Cloud oriented subgraph identification in massive social networks. In 2010 International Conference on Advances in Social Networks Analysis and Mining, pages 248-255. IEEE, 2010.
8. J. Dean and S. Ghemawat. Mapreduce: Simplified data processing on large clusters. Communications of the ACM, 51(1):107-113, 2008.
9. M. Deshpande, M. Kuramochi, N. Wale, and G. Karypis. Frequent substructure-based approaches for classifying chemical compounds. IEEE Transactions on Knowledge and Data Engineering, pages 1036-1050, 2005.
10. F. Eisenbrand and F. Grandoni. On the complexity of fixed parameter clique and dominating set. Theoretical Computer Science, 326, 2004.
11. L. Getoor and C. P. Diehl. Link mining: a survey. SIGKDD Explor. Newsl., 7:3-12, 2005.
12. J. Grochow and M. Kellis. Network motif discovery using subgraph enumeration and symmetry-breaking. In Research in Computational Molecular Biology, pages 92-106. Springer, 2007.
13. J. Huan, W. Wang, J. Prins, and J. Yang. Spin: Mining maximal frequent subgraphs from graph databases. In ACM KDD, 2004.
14. F. Hüffner, S. Wernicke, and T. Zichner. Algorithm engineering for color-coding with applications to signaling pathway detection. Algorithmica, 52(2):114-132, 2008.
15. A. Inokuchi, T. Washio, and H. Motoda. An apriori-based algorithm for mining frequent substructures from graph data. In ECML-PKDD, 2000.
16. M. Kowaluk, A. Lingas, and E. Lundell. Counting and detecting small subgraphs via equations and matrix multiplication. In ACM SODA, 2011.
17. M. Kuramochi and G. Karypis. Finding frequent patterns in a large sparse graph. Data mining and knowledge discovery, 11(3):243-271, 2005.
18. J. Leskovec, A. Singh, and J. Kleinberg. Patterns of influence in a recommendation network. Advances in Knowledge Discovery and Data Mining, pages 380-389, 2006.
19. Y. Liu, X. Jiang, H. Chen, J. Ma, and X. Zhang. Mapreduce-based pattern finding algorithm applied in motif detection for prescription compatibility network. Advanced Parallel Processing Technologies, pages 341-355, 2009.
20. R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. Network motifs: simple building blocks of complex networks. Science, 298(5594):824, 2002.
21. R. Pagh and C. Tsourakakis. Colorful triangle counting and a mapreduce implementation. Information Processing Letters, 2011.
22. N. Pržulj. Biological network comparison using graphlet degree distribution. Bioinformatics, 23(2):e177, 2007.
23. S. Raghavan and H. Garcia-Molina. Representing web graphs. In ICDE, 2003.
24. R. Ronen and O. Shmueli. Evaluating very large datalog queries on social networks. 2009.
25. S. Sakr. Graphrel: A decomposition-based and selectivity-aware relational framework for processing sub-graph queries. 2009.
26. S. Suri and S. Vassilvitskii. Counting triangles and the curse of the last reducer. In Proceedings of the 20th international conference on World wide web, pages 607-614. ACM, 2011.
27. C. Tsourakakis, U. Kang, G. Miller, and C. Faloutsos. Doulion: Counting triangles in massive graphs with a coin. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 837-846. ACM, 2009.
28. V. Vassilevska and R. Williams. Finding, minimizing, and counting weighted subgraphs. In ACM STOC, 2009.
29. T. White. Hadoop: The definitive guide. Yahoo Press, 2010.
30. X. Yan, X. J. Zhou, and J. Han. Mining closed relational graphs with connectivity constraints. In KDD, 2005.
31. Z. Zeng, J. Wang, L. Zhou, and G. Karypis. Out-of-core coherent closed quasi-clique mining from large dense graph databases. ACM Trans. Database Syst., 32, June 2007.
32. Z. Zhao, M. Khan, V. S. A. Kumar, and M. Marathe. Subgraph enumeration in large social contact networks using parallel color coding and streaming. In Parallel Processing (ICPP), 2010 39th International Conference on, pages 594-603, 2010.