DETECTION OF COHESIVE SUBGROUPS IN SOCIAL NETWORKS USING INVASIVE WEED OPTIMIZATION ALGORITHM

Year 2017, Volume: 7 , 221 - 226, 08.09.2017

Yilmaz Atay , İsmail Koc , Mehmet Beskirli

Abstract

Social
network analysis (SNA) is a very popular research area that helps to
analyze social structures through graph theory. Objects in social structures
are represented by nodes and are modeled according to the relations (edges)
they establish with each other. The determination of community structures on
social networks is very important in terms of computer science. In this study,
the Invasive Weed Optimization (IWO) algorithm is proposed for the
detection of meaningful communities from social networks. This algorithm is
proposed for the first time in community detection (CD). In addition,
since the algorithm works in continuous space, it is made suitable for solving
the CD problems by being discretized. The experimental studies are
conducted on human-social networks such as Dutch College, Highland Tribes, Jazz
Musicians and Physicians. The results obtained from experimental results are
compared and analyzed in detail with the results of the Bat Algorithm and Gravitational
Search Algorithm. The comparative results indicate that IWO algorithm is an alternative technique in solving CD
problem in terms of solution quality.

Keywords

Community detection, discretization, invasive weed optimization, social networks, SNA

References

• Attea, B. A., Hariz, W. A., & Abdulhalim, M. F. (2016). Improving the performance of evolutionary multi-objective co-clustering models for community detection in complex social networks. Swarm and Evolutionary Computation, 26, 137-156. doi:10.1016/j.swevo.2015.09.003 Clauset, A., Newman, M. E. J., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6). doi:ARTN 06611110.1103/PhysRevE.70.066111 Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 7821-7826. doi:10.1073/pnas.122653799 Leskovec, J., Lang, K. J., & Mahoney, M. (2010). Empirical comparison of algorithms for network community detection. Paper presented at the Proceedings of the 19th international conference on World wide web. Li, J. W., & Song, Y. L. (2013). Community detection in complex networks using extended compact genetic algorithm. Soft Computing, 17(6), 925-937. doi:10.1007/s00500-012-0942-1 Makagon, M. M., McCowan, B., & Mench, J. A. (2012). How can social network analysis contribute to social behavior research in applied ethology? Applied animal behaviour science, 138(3), 152-161. Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics, 1(4), 355-366. Newman, M. E. J. (2004). Fast algorithm for detecting community structure in networks. Physical Review E, 69(6). doi:ARTN 066133 10.1103/PhysRevE.69.066133 Papadopoulos, S., Kompatsiaris, Y., Vakali, A., & Spyridonos, P. (2012). Community detection in Social Media Performance and application considerations. Data Mining and Knowledge Discovery, 24(3), 515-554. doi:10.1007/s10618-011-0224-z Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks. Computer and Information Sciences - Iscis 2005, Proceedings, 3733, 284-293. Shi, Z. W., Liu, Y., & Liang, J. J. (2009). PSO-based Community Detection in Complex Networks. 2009 Second International Symposium on Knowledge Acquisition and Modeling: Kam 2009, Vol 3, 114-+. doi:10.1109/Kam.2009.195 Steinhaeuser, K., & Chawla, N. V. (2008). Community detection in a large real-world social network. Social Computing, Behavioral Modeling and Prediction, 168-175. doi:Doi 10.1007/978-0-387-77672-9_19 Zhou, X., Liu, Y. H., Zhang, J. D., Liu, T. M., & Zhang, D. (2015). An ant colony based algorithm for overlapping community detection in complex networks. Physica a-Statistical Mechanics and Its Applications, 427, 289-301. doi:10.1016/j.physa.2015.02.020 Zhou, Y., Chen, H., & Zhou, G. (2014). Invasive weed optimization algorithm for optimization no-idle flow shop scheduling problem. Neurocomputing, 137, 285-292.