A review on combinatorial approach of detection community algorithms in social networks with clustering technique

The IJNCPS's Authors that presented the article:

  • Seyyed Mehdi Safi Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Khuzestan, Iran Department of Computer Engineering, Ahvaz Branch, Islamic Azad University Ahvaz, Iran
  • Mohammad Reza Nourimehr Department of Computer Engineering, Science and Research Branch, Islamic Azad University,
  • Seyyed Mohammad Safi Department of Computer Engineering, Ahvaz Branch, Islamic Azad University Ahvaz, Iran
Keywords: Social networks, detection societies, algorithms of detection societies, dividing methods, cumulative methods

Abstract

In the current time, the possibility of using, producing and broadcasting information in Web is increased because of internet availability, and by increasing extension of using communication in virtual world, new kinds of communication among humans are created; in the meanwhile, the study and analysis of social networks seems necessary and has an essential role in information extension. Extending and updating social networks have considerable influence on mass behavior of people and are able to drift them towards a movement, society, existence and/or a product. Identifying these groups in social networks has abundant applications in marketing, social and economic sciences etc., indeed the social networks constitute a graph of the connected people. One way of optimal management for social networks is the use of clustering technique. Clustering is for improvement and increase of efficiency and decreases durance of connection process; in addition, the precision of connection process is also increased.

The aim of this paper is to identify societies and discovering substructures that may exist in networks. Therefore, with regard to the importance and novelty of subject, in addition to introducing elementary concepts of social networks and explaining each algorithm of identifying society and different methods of identifying societies in networks and the results of last studies performed in this area.

References

[1] Newman, M, “Complex systems:a survey,” American Journal of Physics, No. 3, pp. 800-810, 2011.
[2] Fortunato, S, "Community detection in graphs", Elsevier Journal, 2010.
[3] Bródka, P. Filipowski, T. Kazienko .P, "An Introduction to Community Detection in Multi-Layered Social Network", Information Systems, EDetection and Knowledge Management Research, Springer, pp. 185-190, 2013.
[4] Fortunato, S, " Community detection in graphs", Physics Reports Elsevier, No. 3, pp. 75-174, 2010.
[5] 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.
[6] Newman, M, "Communities,modules and large-scale structure in networks", Physics Reports Elsevier, No. 8, 2012
[7] Wang, Z, "Web Mining Research", ICCIMA, 2006.
[8] Asaleh, A, "Recommendation People in Social Networks Using Data Mining", Thesis submitted in fulfilment of the degree of Doctor of Philosophy, 2012.
[9] Ghosh, A, "Cluster Ensembles -A Knowledge", Journal of Machine Detection Research, pp. 583-412, 2003.
[10] Girvan, M. Newman, M, "Finding and Evaluating Community Structure in networks", Physics Reports Elsevier, No. 69, pp. 26-113, 2004.
[11] Karsten, S. Nitesh, V, "Community Detection in a Large Real-World Social Network", University of Notre Dame IN USA, 2012.
[12] 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.
[13] 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.
[14] Eirinaki, M. Vazirgiannis M, "Web mining for web personalization", ACM Transactions, pp. 1-27, 2003
[15] Xu, Y. Chen, L. Asaleh .S Nayak, R, "Improving matching process in social networks", Springer Computer Science, No. 6612, pp. 313-320, 2011.
Published
2017-02-15