Fast community detection algorithm with GPUs and multicore architectures

Proceedings - 25th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2011

Volumn , Issue , 2011, Pages 568-579

  Soman, Jyothish ^a   Narang, Ankur ^a  

^a IBM RESEARCH INDIA   (India)

Author keywords

GPGPU; Multi core Architectures; Parallel Community Detection; Parallel Time Complexity; Performance Analysis; Social Networks

Indexed keywords

COMMUNITY DETECTION; GPGPU; MULTICORE ARCHITECTURES; PERFORMANCE ANALYSIS; SOCIAL NETWORKS; TIME COMPLEXITY;

ALGORITHMS; DISTRIBUTED PARAMETER NETWORKS; GRAPHIC METHODS; PARALLEL ARCHITECTURES; POPULATION DYNAMICS; PROGRAM PROCESSORS; SCALABILITY; SOFTWARE ARCHITECTURE;

NETWORK ARCHITECTURE;

EID: 80053254500     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IPDPS.2011.61     Document Type: Conference Paper

References (28)

1. BADER, D., AND MADDURI, K. GTgraph: A synthetic graph generator suite. Atlanta, GA, February (2006).
2. BATCHER, K. On bitonic sorting networks. Parallel processing: proceedings (1990).
3. BRANDES, U. A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25, 2 (2001), 163-177.
4. CHAKRABARTI, D., ZHAN, Y., AND FALOUTSOS, C. RMAT: A recursive model for graph mining. In SIAM Data Mining (2004), vol. 6, Citeseer, pp. 6-1.
5. CLAUSET, A., NEWMAN, M., AND MOORE, C. Finding community structure in very large networks. Physical Review E 70, 6 (2004), 66111.
6. DANON, L., DÍAZ-GUILERA, A., DUCH, J., AND ARENAS, A. Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment 2005 (2005), P09008.
7. DONETTI, L., AND MUNOZ, M. Detecting network communities: a new systematic and efficient algorithm. Journal of Statistical Mechanics: Theory and Experiment 2004 (2004), P10012.
8. DUCH, J., AND ARENAS, A. Community detection in complex networks using extremal optimization. Physical Review E 72, 2 (2005), 27104.
9. ECKMANN, J., AND MOSES, E. Curvature of co-links uncovers hidden thematic layers in the world wide web. Proceedings of the national academy of sciences 99, 9 (2002), 5825.
10. FORTUNATO, S. Community detection in graphs. Physics Reports (2009).
11. FORTUNATO, S., LATORA, V., AND MARCHIORI, M. Method to find community structures based on information centrality. Physical Review E 70, 5 (2004), 56104.
12. FREEMAN, L. A set of measures of centrality based on betweenness. Sociometry (1977), 35-41.
13. GHOSH, R., AND BHATTACHARJEE, G. Parallel breadth-first search algorithms for trees and graphs. International Journal of Computer Mathematics 15, 1 (1984), 255-268.
14. GIRVAN, M., AND NEWMAN, M. Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99, 12 (2002), 7821.
15. LEUNG, I. X. Y., HUI, P., LIÒ, P., AND CROWCROFT, J. Towards real-time community detection in large networks. Physical Review E 79, 6 (Jun 2009), 066107+.
16. LUSSEAU, D. The emergent properties of a dolphin social network. Proceedings of the Royal Society of London. Series B: Biological Sciences 270, Suppl 2 (2003), S186.
17. NEWMAN, M. Spread of epidemic disease on networks. Physical Review E 66, 1 (2002), 16128.
18. NEWMAN, M. Finding community structure in networks using the eigenvectors of matrices. Physical Review E 74, 3 (2006), 36104.
19. NEWMAN, M., AND GIRVAN, M. Finding and evaluating community structure in networks. Physical review E 69, 2 (2004), 26113.
20. NEWMAN, M. E. J. Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 6 (Jun 2004), 066133.
21. PALLA, G., DERÉNYI, I., FARKAS, I., AND VICSEK, T. Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 7043 (2005), 814-818.
22. PONS, P., AND LATAPY, M. Computing communities in large networks using random walks. Computer and Information Sciences-ISCIS 2005 (2005), 284-293.
23. RADICCHI, F., CASTELLANO, C., CECCONI, F., LORETO, V., AND PARISI, D. Defining and identifying communities in networks. Proceedings of the National Academy of Sciences 101, 9 (2004), 2658.
24. RAGHAVAN, U., ALBERT, R., AND KUMARA, S. Near linear time algorithm to detect community structures in large-scale networks. Physical Review E 76, 3 (2007), 36106.
25. TIBÉLY, G., AND KERTÉSZ, J. On the equivalence of the label propagation method of community detection and a Potts model approach. Physica A: Statistical Mechanics and its Applications 387, 19-20 (2008), 4982-4984.
26. ZACHARY, W. An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 4 (1977), 452-473.
27. ZHANG, Y., WANG, J., WANG, Y., AND ZHOU, L. Parallel community detection on large networks with propinquity dynamics. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009), ACM, pp. 997-1006.
28. ZHOU, H., AND LIPOWSKY, R. Network Brownian motion: A new method to measure vertex-vertex proximity and to identify communities and subcommunities. Computational Science-ICCS 2004 (2004), 1062-1069.