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Outline

Title
Abstract
Introduction
Social Networks and Their Analysis
Social Network Analysis
Levels of Analysis
Metrics (Measures) in Social Network Analysis
Multi-Objective Genetic Algorithm
Datasets
Experimental Results
Conclusion
References

Community Detection Using Nature Inspired Algorithm

Profile image of shikha mehtashikha mehta

Hybrid Intelligence for Social Networks

https://doi.org/10.1007/978-3-319-65139-2_3
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30 pages

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Abstract

Community detection in social networks has become a dominating topic of the current data research as it has a direct implication in many different areas of importance, whether social network, citation network, traffic network, metabolic network, protein-protein network or web graph etc. Mining meaningful communities in a real-world network is a hard problem owing to the dynamic nature of these networks. The existing algorithms for community detection depend chiefly on the network topologies and are not effective for such sparse graphs. Thus, there is a great need to optimize those algorithms. Evolutionary algorithms have emerged as a promising solution for optimized approximation of hard problems. We propose to optimize the community detection method based on modularity and normalized mutual information (NMI) using the latest grey wolf optimization algorithm. The results demonstrate the effectiveness of the algorithms, when compared with other contemporary evolutionary algorithms.

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References (41)

  1. Agrawal, R.: Bi-objective community detection (BOCD) in networks using genetic algorithm. In: Communications in Computer and Information Science, vol. 168, September 2011. doi:10.1007/978-3-642-22606-9_5. Source: arXiv
  2. Babers, R., et al.: A nature-inspired metaheuristic lion optimization algorithm for community detection. In: Computer Engineering Conference (ICENCO), 2015 11th International, IEEE (2015). 978-1-5090-0275-7/15/2015
  3. Banati, H., Arora, N.: TL-GSO -a hybrid approach to mine communities from social network. In: 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)
  4. Baofang, H.U.: A cultural algorithm based on artificial bee colony optimization for community detection in signed social networks. In: 2015 10th International Conference on Broadband and Wireless Computing, Communication and Applications (2015)
  5. Barbara, D.: An Introduction to Cluster Analysis for Data Mining (2000). Retrieved November 12, 2003, from http://www-users.cs.umn.edu/~han/dmclass/cluster_survey_10_02_00.pdf
  6. Bedi, P., Sharma, C.: Community detection in social networks. Adv. Rev. Published online: 19 Feb 2016. doi:10.1002/widm.1178. WIREs Data Mining Knowledge Discovery. doi:10.1002/widm.1178
  7. Bhawsar, Y., Thakur, G.S.: Community detection in social networking. J. Inf. Eng. Appl. 3(6) (2013). www.iiste.org. ISSN: 2224-5782 (print). ISSN: 2225-0506 (online) -Selected from International Conference on Recent Trends in Applied Sciences with Engineering Applications
  8. Chen, W.: Community detection in social networks through community formation games. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (2011)
  9. Chen, F., Li, K.: Detecting hierarchical structure of community members in social networks. Knowl.-Based Syst. 87, 3-15 (2015)
  10. Choudhury, D., Paul, A.: Community detection in social networks: an overview. Int. J. Res. Eng. Technol. eISSN: 2319-1163. pISSN: 2321-7308
  11. Chunyu, W., Yun, P.: Discrete bat algorithm and application in community detection. Open Cybernet. Syst. J. 9, 967-972 (2015)
  12. Dickinson, B.: A genetic algorithm for identifying overlapping communities in social networks using an optimized search space. Soc. Netw. 2, 193-201 (2013). http://dx.doi.org/10.4236/sn. 2013.24019. Published Online October 2013 http://www.scirp.org/journal/sn
  13. Dixit, M., et al.: An exhaustive survey on nature inspired optimization algorithms. Int. J. Softw. Eng. Appl. 9(4), 91-104 (2015). http://dx.doi.org/10.14257/ijseia.2015.9.4.11
  14. Eindride, E.: Community Detection in Social Networks, a thesis. Department of Informatics, University of Bergen (2015)
  15. Fortunato, S.: Community detection in graphs. arXiv:0906.0612v2 [physics.soc-ph] 25 Jan 2010
  16. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99(12), 7821-7826 (2002)
  17. Gong, M.: Memetic algorithm for community detection in networks. Phys. Rev. E 84, 056101 (2011)
  18. Khatoon, M., Aisha Banu, W.: A survey on community detection methods in social networks. Int. J. Educ. Manage. Eng. 1, 8-18 (2015)
  19. Kim, K., et al.: Multiobjective evolutionary algorithms for dynamic social network clustering. In: GECCO 10 Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 1179-1186
  20. Lancichinetti, A., Fortunato, S.: Community detection algorithms: a comparative analysis. arXiv:0908.1062v2[physics.soc-ph]16 Sep 2010
  21. Lancichinetti, A., Radicchi, F., Ramasco, J.J., Fortunato, S.: Finding statistically significant communities in networks. PLoS One 6(4), e18961 (2011). doi:10.1371/journal.pone.0018961
  22. Li, K., Pang, Y.: A unified community detection algorithm in complex network. Neurocomput- ing 130, 36-43 (2014). www.elsevier.com/locate/neucom
  23. Liu, C.: A Multiobjective Evolutionary algorithm based on similarity for community detection from signed social networks. IEEE Trans. Cybernet. 44(12) (2014)
  24. Mahajan, A., Kaur, M.: Various approaches of community detection in complex networks: a glance. Int. J. Inf. Technol. Comput. Sci. 4, 35-41 (2016). Published Online April 2016 in MECS. http://www.mecs-press.org/DOI:10.5815/ijitcs.2016.04.05
  25. Mirjalili, S.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46-61 (2014)
  26. Newman, M.E.J.: Detecting community structure in networks. Eur. Phys. J. B 38, 321 (2004). doi:10.1140/epjb/e2004-00124-y
  27. Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 036104 (2006)
  28. Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814-818 (2005)
  29. Pizzuti, C.: A multi-objective genetic algorithm for community detection in networks. In: ICTAI 2009, 21st IEEE International Conference on Tools with Artificial Intelligence, Newark, NJ, 2-4 November 2009
  30. Pizzuti, C.: A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans. Evol. Comput. 6(3), 418-430 (2012)
  31. Raghavan, U.N., et al.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)
  32. Social Network Analysis: Theory and Applications. https://www.politaktiv.org/documents/ 10157/29141/SocNet_TheoryApp.pdf
  33. Steinhaeuser, K., Chawla, N.V.: Community detection in a large real-world social network. In: Social Computing, Behavioral Modeling, and Prediction, pp. 168-175
  34. Wang, M., et al.: Community detection in social networks: an indepth benchmarking study with a procedure oriented framework. In: Proceedings of the VLDB Endowment VLDB Endowment Homepage archive, vol. 8(10), June 2015, pp. 998-1009
  35. Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press, Cambridge (1994)
  36. Wu, P., Pan, L.: Multi-objective community detection based on memetic algorithm. PLoS One 10(5), e0126845. doi:10.1371/journal. pone.0126845 (2015)
  37. Xu, Y.: Finding overlapping community from social networks based on community forest model. Knowl.-Based Syst. 1-18 (2016). ISSN: 0950-7051 000
  38. Yazdani, M., Jolai, F.: Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Comput. Des. Eng. 3, 24-36 (2016)
  39. Zadeh, P.M., Kobti, Z.: A multi-population cultural algorithm for community detection in social networks. In: The 6th International Conference on Ambient Systems, Networks and Technologies (ANT 2015), Procedia Computer Science, vol. 52, pp. 342-349 (2015)
  40. Zakrzewska, A., Bader, D.A.: A dynamic algorithm for local community detection in graphs. In: 2015 Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM 2015, Paris, 25-28 August 2015, pp. 559-564
  41. Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 9(2), 026113 (2004)

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