discovery of communities in social networks of a dynamic layer with the approach of maximizing the importance

Document Type : Original Article

Authors

1 PhD student, University of Qom, Tehran, Iran

2 Assistant Professor, Imam Hossein University (AS), Tehran, Iran

3 Assistant Professor, University of Qom, Qom, Iran

Abstract

The current world is the era of the network and the Internet, an era in which, with the formation of various social networks, new methods of communication and information have been introduced to the wide field of social communication. Social networks have become one of the most popular destinations of Internet users in recent years [1]. A social network is a group of people or organizations with common interests and tastes that come together to achieve specific goals. The main reasons for creating social networks include personal relationships, work relationships, scientific relationships, shared tastes and interests, and social and political motivations. Analysis of social networks means the study of social network characteristics and relationships between people and parts of a network with a network or graph theory approach. Social network analysis is a kind of interdisciplinary study in different fields such as sociology, mathematics, computer and cyber science. One of the basic challenges in analyzing social networks is the instability of these networks because every second people or organizations may join or leave these networks or new relationships may be formed.

In this paper, we present an innovative method for discovering complex communities in social networks with a dynamic layer and focusing on the maximization of the importance criterion. Then, using the data set produced by LFR Benchmark, he checked the results of his proposed plan with other plans, and the results obtained indicate the improvement of the performance of our proposed scheme in the accuracy of identifying complex relationships in social networks and its less time compared to other scheme.

Keywords


Smiley face

[1] Kim, Jungeun, and Jae-Gil Lee. "Community detection in multi-layer graphs: A survey." ACM SIGMOD Record 44, no. 3 (2015): 37-48.
[2] Berlingerio, Michele, Michele Coscia, and Fosca Giannotti. "Finding and characterizing communities in multidimensional networks." In 2011 international conference on advances in social networks analysis and mining, pp. 490-494. IEEE, 2011.
[3] Börner, Katy, Michael Conlon, Jon Corson-Rikert, and Ying Ding. "VIVO: A semantic approach to scholarly networking and discovery." Synthesis lectures on the Semantic Web: theory and technology 7, no. 1 (2012): 1-178.
[4] Fortunato, Santo, and Darko Hric. "Community detection in networks: A user guide." Physics reports 659 (2016): 1-44.
[5] Newman, Mark EJ. "Spectral methods for community detection and graph partitioning." Physical Review E 88, no. 4 (2013): 042822.
[6] Loe, Chuan Wen, and Henrik Jeldtoft Jensen. "Comparison of communities detection algorithms for multiplex." Physica A: Statistical Mechanics and its Applications 431 (2015): 29-45.
[7] Whang, Joyce Jiyoung, David F. Gleich, and Inderjit S. Dhillon. "Overlapping community detection using neighborhood-inflated seed expansion." IEEE Transactions on Knowledge and Data Engineering 28, no. 5 (2016): 1272-1284.
[8] Leskovec, Jure, Kevin J. Lang, Anirban Dasgupta, and Michael W. Mahoney. "Statistical properties of community structure in large social and information networks." In Proceedings of the 17th international conference on World Wide Web, pp. 695-704. 2008.
[9] MacQueen, J. "Classification and analysis of multivariate observations." In 5th Berkeley Symp. Math. Statist. Probability, pp. 281-297. 1967.
[10] Newman, Mark EJ. "Spectral methods for community detection and graph partitioning." Physical Review E 88, no. 4 (2013): 042822.
[11] Newman, Mark EJ. "Modularity and community structure in networks." Proceedings of the national academy of sciences 103, no. 23 (2006): 8577-8582.
[12] Aldecoa, Rodrigo, and Ignacio Marín. "Surprise maximization reveals the community structure of complex networks." Scientific reports 3, no. 1 (2013)
 [13] Lancichinetti, Andrea, Filippo Radicchi, and José J. Ramasco. "Statistical significance of communities in networks." Physical Review E 81, no. 4 (2010): 046110.
 [14] Dubik, Mikael. "A comparative evaluation of state-of-the-art community detection algorithms for multiplex networks." (2017).
[15] Traag, Vincent A., Gautier Krings, and Paul Van Dooren. "Significant scales in community structure." Scientific reports 3, no. 1 (2013): 1-10.
[16] Dubik, Mikael. "A comparative evaluation of state-of-the-art community detection algorithms for multiplex networks." (2017).
[17] Fernandes, Andreia, Patrícia CT Gonçalves, Pedro Campos, and Catarina Delgado. "Centrality and community detection: a co-marketing multilayer network." Journal of Business & Industrial Marketing (2019).
[18] Bakhthemmat, Ali, and Mohammad Izadi. "Communities detection for advertising by futuristic greedy method with clustering approach." Big Data 9, no. 1 (2021): 22-40.
[19] Su, Yan. "Accurate Marketing Algorithm of Network Video Based on User Big Data Analysis." Mathematical Problems in Engineering 2022 (2022).
[20] Leiva, Fabiola Herrera, Romina Torres, Orietta Nicolis, and Rodrigo Salas. "Characterization of the chilean public procurement ecosystem using social network analysis." IEEE Access 8 (2020): 138846-138858.
[21] Sharma, Prem Sagar, Divakar Yadav, and R. N. Thakur. "Web Page Ranking using Web Mining Techniques: A comprehensive survey." Mobile Information Systems 2022 (2022).
[22] Yu-Liang, Lu, Tian Jie, Guo Hao, and Wang Yu. "Infomap based community detection in weibo following graph." In 2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control, pp. 1220-1222. IEEE, (2012).
[23] Lancichinetti, Andrea, Filippo Radicchi, José J. Ramasco, and Santo Fortunato. "Finding statistically significant communities in networks." PloS one 6, no. 4 (2011): e18961.
 
Volume 12, Issue 1 - Serial Number 45
No. 45, Spring 2024
June 2024
Pages 43-57
  • Receive Date: 14 February 2024
  • Revise Date: 14 April 2024
  • Accept Date: 02 May 2024
  • Publish Date: 04 June 2024