Presenting an optimal method for identifying communities in the Instagram’s social network with clustering method

The IJNCPS's Authors that presented the article:

  • Fateme Panabad Department of Computer Engineering,Ahvaz Branch, Islamic Azad University Ahvaz,Iran
  • Seyyed Mohammad Safi Department of Computer Engineering, Ahvaz Branch, Islamic Azad University Ahvaz,Iran
Keywords: social networks, recognizing societies, community identification, community identification algorithms, clustering method

Abstract

In today's world, social networks play an essential role in expanding information.One of themost important issues is identification of communities in these networks.In previous methods, the identification of communities was such that the number of “Like” feedbacksfor each member in one group was determined regardless the number of member posts, and any member with more likes was identified as a more effective member.In the proposed method, the number of feedbacks to each member's post is determined by separating the posts of each member.As a result, the important posts are identified. Thus, the generated graph provides more comprehensive information about the communications of individuals. The new graph is then grouped by community identification methods such as Girvan Newman, CNM, Wakita Tsurumi and the final result will be presented as the final optimal result by voting on the results of the three above-mentioned methods.

References

[1] P. Bródka, T. Filipowski, and P. Kazienko, “An Introduction to CommunityDetection in Multi-Layered Social Network,” in Information Systems, ELearning,and Knowledge Management Research, Springer, 2013, pp. 185–190.
[2] SantoFortunato ,“Community detection in graphs”, Physics Reports, Elsevier ,2010.
[3] Fortunato, S, " Community detection in graphs", Physics Reports Elsevier, No. 3, pp. 75-174, 2010.
[4] Tang, L. Wang, X. Liu .H, "Community detection via heterogeneous interaction analysis", Computer Science Data Mining and Knowledge Discovery, No. 12, pp. 1-33, 2012.
[5] Girvan, M. Newman, M, "Finding and EvaluatingCommunity Structure in networks", Physics Reports Elsevier, No. 69, pp. 26-113, 2004.
[6] Tang, L. Wang, X. Liu .H, "Community detection via heterogeneous interaction analysis", Computer Science Data Mining and Knowledge Discovery, No. 12, pp. 1-33, 2012.
[7] KAFHALI, S. HAQIQ, A. Liu .H, "Effect of Mobility and Traffic Models on the Energy Consumption in MANET Routing Protocols", International Journal of Soft Computing and Engineering, No. 1, pp. 2231-2307, 2013.
[8] Karsten, S. Nitesh, V, "Community Detection in a Large Real-World Social Network", University of Notre Dame IN USA, 2012.
[9] Xu, Y. Chen, L. Asaleh .S Nayak, R, "Network Detection on Metric Space", Ghrera Department of Computer Science and Engineering Jaypee University Of information Technology Waknaghat Solan Himachal India, No. 173215, 2015.
[10]. M. E. Newman and M. Girvan, “Finding and Evaluating Community Structure in Networks,” Phys. Rev. E, vol. 69, no. 2, p. 026113, 2004.
[11] Fortunato, S, "Community detection in graphs", Elsevier Journal, 2010.
[12] Aggarwal, C. C. and C. K. Reddy (2013). Data clustering: algorithms and applications.
[13] Berkhin, P. (2006). A survey of clustering data mining techniques. In Grouping multidimensional
data, pp. 25–71. Springer.
[14] Han, J., M. Kamber, and J. Pei (2011). Data mining: concepts and techniques: concepts and
techniques. Elsevier.
[15] Wakita, K. and T. Tsurumi (2007). Finding community structure in mega-scale social networks:[extended abstract]. In Proceedings of the 16th international conference on World WideWeb, pp. 1275–1276.
[16] Girvan, M. and M. E. Newman (2002). Community structure in social and biological networks.
Proceedings of the national academy of sciences 99(12), 7821–7826.
[17] Clauset, A., M. E. Newman, and C. Moore (2004). Finding community structure in very largenetworks. Physical review E 70(6), 066111.
[18] Asaleh, A, "Recommendation People in Social Networks Using Data Mining", Thesis submitted in fulfilment of the degree of Doctor of Philosophy, 2012.
[19] Nepusz, T., A. Petróczi, L. Négyessy, and F. Bazsó (2008). Fuzzy communities and the concept
of bridgeness in complex networks.
[20] Strehl, A. and J. Ghosh (2003). Cluster ensembles—a knowledge reuse framework for combiningmultiple partitions. The Journal of Machine Learning Research 3, 583–617.
[21] Brandes, U., M. Gaertler, and D. Wagner (2003). Experiments on graph clustering algorithms.Springer.
Published
2018-01-21