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Identifying, Ranking and Tracking Community Leaders in Evolving Social Networks

Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)

Abstract

Discovering communities in a network is a fundamental and important problem to complex networks. Find the most influential actors among its peers is a major task. If on one side, studies on community detection ignore the influence of actors and communities, on the other hand, ignoring the hierarchy and community structure of the network neglect the actor or community influence. We bridge this gap by combining a dynamic community detection method with a dynamic centrality measure. The proposed enhanced dynamic hierarchical community detection method computes centrality for nodes and aggregated communities and selects each community representative leader using the ranked centrality of every node belonging to the community. This method is then able to unveil, track, and measure the importance of main actors, network intra and inter-community structural hierarchies based on a centrality measure. The empirical analysis performed, using two temporal networks shown that the method is able to find and tracking community leaders in evolving networks.

Keywords

Community detection Dynamic networks Community leaders Centrality measures 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of EngineeringUniversity of PortoPortoPortugal
  2. 2.Laboratory of Artificial Intelligence and Decision SupportPortoPortugal
  3. 3.Faculty of Science and TechnologyRyukoku UniversityKyotoJapan

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