Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership

TL;DR

This paper presents an efficient incremental method for detecting and tracking community structures in highly dynamic social networks by leveraging the concept of leadership and its persistence over time.

Contribution

It introduces a novel approach that detects communities in evolving social networks without recomputing from scratch, focusing on leadership importance and persistence.

Findings

Effective in real-world social networks

Efficient in handling highly dynamic networks

Accurate in tracking community evolution

Abstract

Detecting community structure in social networks is a fundamental problem empowering us to identify groups of actors with similar interests. There have been extensive works focusing on finding communities in static networks, however, in reality, due to dynamic nature of social networks, they are evolving continuously. Ignoring the dynamic aspect of social networks, neither allows us to capture evolutionary behavior of the network nor to predict the future status of individuals. Aside from being dynamic, another significant characteristic of real-world social networks is the presence of leaders, i.e. nodes with high degree centrality having a high attraction to absorb other members and hence to form a local community. In this paper, we devised an efficient method to incrementally detect communities in highly dynamic social networks using the intuitive idea of importance and persistence of community leaders over time. Our proposed method is able to find new communities based on the previous structure of the network without recomputing them from scratch. This unique feature, enables us to efficiently detect and track communities over time rapidly. Experimental results on the synthetic and real-world social networks demonstrate that our method is both effective and efficient in discovering communities in dynamic social networks.