Ant colony optimization with a new random walk model for community detection in complex networks

Advances in Complex Systems

Volumn 14, Issue 5, 2011, Pages 795-815

  Jin, Di ^a   Liu, Dayou ^a   Yang, Bo ^a   Liu, Jie ^a   He, Dongxiao ^a  

^a JILIN UNIVERSITY   (China)

Author keywords

ant colony optimization; clustering ensemble; community detection; Complex network; Markov random walk

Indexed keywords

EID: 80055076412     PISSN: 02195259     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219525911003219     Document Type: Article

References (36)

1. Watts, D. J. and Strogatz, S. H., Collective dynamics of small-world networks, Nature 393 (1998) 440-442.
2. Barabási, A. L., Albert, R., Jeong, H. and Bianconi G., Power-law distribution of the World Wide Web, Science 287 (2000) 2115a.
3. Girvan, M. and Newman, M.E. J., Community structure in social and biological networks, Proc. Natl. Acad. Sci. 9 (2002) 7821-7826.
4. Santo, F., Community detection in graphs, Phys. Rep. 486 (2010) 75-174.
5. Newman, M.E. J., Fast algorithm for detecting community structure in networks, Phys. Rev. E 69 (2004) 066133.
6. Guimera, R. and Amaral, L.A. N., Functional cartography of complex metabolic networks, Nature 433 (2005) 895-900.
7. Blondel, V.D., Guillaume, J. L., Lambiotte, R. and Lefebvre, E., Fast unfolding of communities in large networks, J. Stat. Mech. (2008) P10008.
8. Shi, C., Yan, Z., Wang, Y., Cai, Y. and Wu, B., A genetic algorithm for detecting communities in large-scale complex networks, Adv. Complex Syst. 13 (2010) 3-17.
9. Jin, D., He, D., Liu, D. and Baquero, C., Genetic algorithm with local search for community mining in complex networks, in Proc. IEEE Int. Conference on Tools with Artificial Intelligence (ICTAI'10) (Arras, France, 2010), pp. 105-112.
10. Palla, G., Derenyi, I., Farkas, I. and Vicsek, T., Uncovering the overlapping community structures of complex networks in nature and society, Nature 435 (2005) 814-818.
11. Raghavan, U. N., Albert, R. and Kumara, S., Near linear-time algorithm to detect community structures in large-scale networks, Phys. Rev. E 76 (2007) 036106.
12. Zhang, Y., Wang, J., Wang, Y. and Zhou, L., Parallel community detection on large networks with propinquity dynamics, in Proc. ACM Int. Conference on Knowledge Discovery and Data Mining (KDD'09) (Paris, France, 2009), pp. 997-1005.
13. Morãrescu, C. I. and Girard, A., Opinion dynamics with decaying confidence: Application to community detection in graphs, IEEE Trans. Automat. Contr. (in press).
14. Stijn van Dongen, Graph Clustering by Flow Simulation, Ph.D. thesis (University of Utrecht, Utrecht, Netherlands 2000).
15. Pons, P. and Latapy, M., Computing communities in large networks using random walks, J. Graph Algorithms Appl. 10 (2006) 191-218.
16. Gunes, I. and Bingol, H., Community detection in complex networks using agents, in Int. Conference on Autonomous Agents and Multiagent Systems (AAMAS'07) (Honolulu, Hawaii, USA, 2007).
17. Rosvall, M. and Bergstrom, C. T., Maps of random walks on complex networks reveal community structure, Proc. Natl. Acad. Sci. 105 (2008) 1118-1123.
18. E W., Li, T. and Vanden-Eijnden, E., Optimal partition and effective dynamics of complex networks, Proc. Natl. Acad. Sci. 105 (2008) 7907.
19. Yang, B., Cheung,W.K. and Liu, J., Community mining from signed social networks, IEEE Trans. on Knowledge and Data Engineering 19 (2007) 1333-1348.
20. Yang, B., Liu, J., Feng, J. and Liu, D., On modularity of social network communities: The spectral characterization, in Proc. IEEE/WIC/ACM Int. Conference on Web Intelligence (WI'08) (Sydney, Australia, 2008), pp. 127-133.
21. Strehl, A. and Ghosh, J., Cluster ensembles -a knowledge reuse framework for combining partitionings, in Proc. AAAI Conference on Artificial Intelligence (AAAI'02) (California, USA, 2002), pp. 93-98.
22. Lancichinetti, A., Fortunato, S. and Radicchi, F., Benchmark graphs for testing community detection algorithms, Phys. Rev. E 78 (2008) 046110.
23. Newman, M.E. J., Strogatz, S. H. and Watts, D. J., Random graphs with arbitrary degree distributions and their applications, Phys. Rev. E 64 (2001) 026118.
24. Leskovec, J., Lang, K. J. and Mahoney, M.W., Empirical comparison of algorithms for network community detection, in Proc. 19th International World Wide Web Conference (WWW'10) (Raleigh, North Carolina USA 2010), pp. 631-640.
25. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V. and Parisi, D., Defining and identifying communities in networks, Proc. Natl. Acad. Sci. 101 (2004) 2658-2663.
26. Milgram, S., The small world problem, Psychol. Today 1 (1967) 60-67.
27. Albert, R., Jeong, H. and Baraba'si, A. L., Diameter of the World Wide Web, Nature 401 (1999) 130-131.
28. Lancichinetti, A. and Fortunato, S., Community detection algorithms: A comparative analysis, Phys. Rev. E 80 (2009) 056117.
29. The software used in this paper can be found in ftp://jindijlu:dd@59.72. 0.62/.
30. Danon, L., Duch, J., Diaz-Guilera, A. and Arenas, A., Comparing community structure identification, J. Stat. Mech. (2005) P09008.
31. Zachary, W.W., An information flow model for conflict and fission in small groups, J. Anthropol. Res. 33 (1977) 452-473.
32. Lusseau, D., The emergent properties of a dolphin social network, Proc. Biol. Sci. 270 (2003) S186-S188.
33. Newman, M.E. J., Modularity and community structure in networks, Proc. Natl. Acad. Sci. 103 (2006) 8577-8582.
34. Gleiser, P. M. and Danon, L., Community structure in jazz, Adv. Complex Syst. 6 (2003) 565-573.
35. Guimerá, R., Danon, L., Diaz-Guilera, A., Giralt, F. and Arenas, A., Self-similar community structure in a network of human interactions, Phys. Rev. E 68 (2003) 065103.
36. Newman, M.E. J. and Girvan, M., Finding and evaluating community structure in networks. Phys. Rev. E 69 (2004) 026113.