COMMIT: A scalable approach to mining communication motifs from dynamic networks

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn 2015-May, Issue , 2015, Pages 475-489

  Gurukar, Saket ^a   Ranu, Sayan ^a   Ravindran, Balaraman ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

Author keywords

Communication motifs; Graph mining; Interaction networks

Indexed keywords

SOCIAL NETWORKING (ONLINE);

BEHAVIORAL ANALYSIS; DYNAMIC INTERACTION; GRAPH MINING; HUMAN INTERACTIONS; INTERACTION NETWORKS; ORDERS OF MAGNITUDE; QUALITATIVE ANALYSIS; SCALABLE APPROACH;

BEHAVIORAL RESEARCH;

EID: 84957581340     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2723372.2737791     Document Type: Conference Paper

References (38)

1. ENRON, http://www.cs.cmu.edu/~enron/.
2. http://en. wikipedia.org/wiki/f1-score.
3. SNAP, http://snap. stanford.edu/.
4. I. Albert and R. Albert. Conserved network motifs allow protein-protein interaction prediction. Bioinformatics, 20(18):3346-3352, 2004.
5. E. Allan, W. Turkett, and E. Fulp. Using network motifs to identify application protocols. In Global Telecommunications Conference, 2009. GLOBECOM 2009. IEEE, pages 1-7, Nov. 2009.
6. K. Borgwardt, H.-P. Kriegel, and P. Wackersreuther. Pattern mining in frequent dynamic subgraphs. In ICDM, pages 818-822, 2006.
7. B. Bringmann and S. Nijssen. What is frequent in a single graph? In PAKDD, pages 858-863. Springer-Verlag, 2008.
8. F. Bruno, L. Palopoli, and S. E. Rombo. New trends in graph mining: Structural and node-colored network motifs. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 1(1):81-99, 2010.
9. G. Chechik, E. Oh, O. Rando, J. Weissman, A. Regev, and D. Koller. Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network. Nature biotechnology, 26(11):1251-1259, 2008.
10. G. Ciriello and C. Guerra. A review on models and algorithms for motif discovery in protein-protein interaction networks. Briefings in functional genomics & proteomics, 7(2):147-156, 2008.
11. B. Ding, D. Lo, J. Han, and S.-C. Khoo. Efficient mining of closed repetitive gapped subsequences from a sequence database. In ICDE, pages 1024-1035, 2009.
12. M. Elseidy, E. Abdelhamid, S. Skiadopoulos, and P. Kalnis. Grami: Frequent subgraph and pattern mining in a single large graph. PVLDB, 7(7), 2014.
13. L. K. Gallos, D. Rybski, F. Liljeros, S. Havlin, and H. A. Makse. How people interact in evolving online affiliation networks. Phys. Rev. X, 2:031014, Aug. 2012.
14. D. Jurgens and T.-C. Lu. Temporal motifs reveal the dynamics of editor interactions in wikipedia. In ICWSM, 2012.
15. Z. R. Kashani, H. Ahrabian, E. Elahi, A. Nowzari-Dalini, E. S. Ansari, S. Asadi, S. Mohammadi, F. Schreiber, and A. Masoudi-Nejad. Kavosh: a new algorithm for finding network motifs. BMC bioinformatics, 10(1):318, 2009.
16. N. Kashtan, S. Itzkovitz, R. Milo, and U. Alon. Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics, 20(11):1746-1758, 2004.
17. N. S. Ketkar, L. B. Holder, and D. J. Cook. Subdue: Compression-based frequent pattern discovery in graph data. In Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, pages 71-76, 2005.
18. L. Kovanen, M. Karsai, K. Kaski, J. Kertész, and J. Saramäki. Temporal motifs in time-dependent networks. Journal of Statistical Mechanics: Theory and Experiment, 2011(11):P11005, 2011.
19. L. Kovanen, K. Kaski, J. Kertész, and J. Saramäki. Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences. Proceedings of the National Academy of Sciences, 110(45):18070-18075, 2013.
20. M. Kuramochi and G. Karypis. Frequent subgraph discovery. In ICDM, pages 313-320, 2001.
21. M. Kuramochi and G. Karypis. Finding frequent patterns in a large sparse graph. Data mining and knowledge discovery, 11(3):243-271, 2005.
22. K. Liu, W. K. Cheung, and J. Liu. Detecting multiple stochastic network motifs in network data. In Advances in Knowledge Discovery and Data Mining, pages 205-217. Springer, 2012.
23. R. Milo, S. Itzkovitz, N. Kashtan, R. Levitt, S. Shen-Orr, I. Ayzenshtat, M. Sheffer, and U. Alon. Superfamilies of evolved and designed networks. Science, 303(5663):1538-1542, 2004.
24. 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):824-827, 2002.
25. D. O'Callaghan, M. Harrigan, J. Carthy, and P. Cunningham. Network analysis of recurring youtube spam campaigns. CoRR, abs/1201.3783, 2012.
26. J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In ICDE, pages 0215-0215, 2001.
27. S. Ranu, B. T. Calhoun, A. K. Singh, and S. J. Swamidass. Probabilistic substructure mining from small-molecule screens. Molecular Informatics, 30(9):809-815, 2011.
28. S. Ranu, M. Hoang, and A. Singh. Mining discriminative subgraphs from global-state networks. In SIGKDD, pages 509-517, 2013.
29. S. Ranu and A. K. Singh. Graphsig: A scalable approach to mining significant subgraphs in large graph databases. In ICDE, pages 844-855, 2009.
30. S. Ranu and A. K. Singh. Mining statistically significant molecular substructures for efficient molecular classification. Journal of chemical information and modeling, 49(11):2537-2550, 2009.
31. S. S. Shen-Orr, R. Milo, S. Mangan, and U. Alon. Network motifs in the transcriptional regulation network of escherichia coli. Nature genetics, 31(1):64-68, 2002.
32. C. Spearman. The proof and measurement of association between two things. The American journal of psychology, 15(1):72-101, 1904.
33. B. Viswanath, A. Mislove, M. Cha, and K. P. Gummadi. On the evolution of user interaction in facebook. In Proceedings of the 2nd ACM SIGCOMM Workshop on Social Networks (WOSN'09), August 2009.
34. S. Wernicke. Efficient detection of network motifs. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 3(4):347-359, 2006.
35. X. Yan and J. Han. gspan: Graph-based substructure pattern mining. In ICDM, pages 721-724, 2002.
36. Z. Zeng, A. K. H. Tung, J. Wang, J. Feng, and L. Zhou. Comparing stars: On approximating graph edit distance. PVLDB, 2(1), 2009.
37. Q. Zhao, Y. Tian, Q. He, N. Oliver, R. Jin, and W.-C. Lee. Communication motifs: a tool to characterize social communications. In CIKM, pages 1645-1648, 2010.
38. F. Zhu, X. Yan, J. Han, and P. S. Yu. gprune: A constraint pushing framework for graph pattern mining. In PAKDD, pages 388-400, 2007.