Work

TaCD: Team-Aware Community Detection Based on Multi-View Modularity

Year: 2025

Type: article

Abstract: Community detection in social networks is one of the most important topics of network science. Researchers have developed numerous methods from various perspectives. However, the existing methods often overlook the team information encoded as a special type of user relation in the social network, which plays an important role in community formation and evolution. In this paper, we propose a novel community detection algorithm called Team-aware Community Detection (TaCD). Our model constructs a multi-view network by encoding the user interaction information as the user view and the team information as the team view. To measure the consistency across the two views, we use the Jaccard similarity to establish a cross-view coupling. Based on the constructed 2-view network, we use multi-view modularity to discover team-aware community structure, and solve the optimization problem using the well-known Generalized Louvain approach. Another contribution of this paper is the collection of a new SCHOLAT dataset, which consists of several social networks with team information and is publicly available for testing purposes. Our experimental results on several SCHOLAT networks with team information demonstrate that TaCD outperforms the existing community detection algorithms.

Source: Entropy

DOI: 10.3390/e28010021

Authors: Chengzhou Fu, Feiyi Tang, Lingzhi Hu, Chengzhe Yuan, Ronghua Lin

Institutions: Guangdong Pharmaceutical University, Guangzhou College of Commerce, Macao Polytechnic University, Guangdong Polytechnic Normal University, South China Normal University

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: National Natural Science Foundation of China, Guangdong Pharmaceutical University