A similarity-based community detection method with multiple prototype representation

TL;DR

This paper introduces a similarity-based multi-prototype community detection method that models communities with multiple representative nodes, improving detection accuracy and providing richer community structure information.

Contribution

The paper proposes a novel multi-prototype approach for community detection that accounts for diverse node roles within communities, enhancing clustering performance over single-prototype methods.

Findings

SMP outperforms existing algorithms on synthetic and real-world networks.

Provides detailed internal community structure through prototype weights.

Demonstrates robustness and improved accuracy in community detection.

Abstract

Communities are of great importance for understanding graph structures in social networks. Some existing community detection algorithms use a single prototype to represent each group. In real applications, this may not adequately model the different types of communities and hence limits the clustering performance on social networks. To address this problem, a Similarity-based Multi-Prototype (SMP) community detection approach is proposed in this paper. In SMP, vertices in each community carry various weights to describe their degree of representativeness. This mechanism enables each community to be represented by more than one node. The centrality of nodes is used to calculate prototype weights, while similarity is utilized to guide us to partitioning the graph. Experimental results on computer generated and real-world networks clearly show that SMP performs well for detecting communities. Moreover, the method could provide richer information for the inner structure of the detected communities with the help of prototype weights compared with the existing community detection models.