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

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References

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History

  • Receive Date: 14 February 2024
  • Revise Date: 02 May 2024
  • Accept Date: 15 May 2024
  • Publish Date: 21 May 2024

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