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

Chengzhou Fu¹, Feiyi Tang², Lingzhi Hu^3,4

¹College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China.

Entropy (Basel, Switzerland)

|January 28, 2026

Summary

This summary is machine-generated.

This study introduces a new algorithm, Team-aware Community Detection (TaCD), for social networks. TaCD effectively incorporates team information, outperforming existing methods in community detection tasks.

Keywords:

Generalized Louvain SCHOLAT community detection multi-view team-aware

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Area of Science:

Network science
Social network analysis
Data mining

Background:

Community detection is crucial in network science.
Existing methods often ignore vital team information in social networks.
Team structures significantly influence community formation and evolution.

Purpose of the Study:

To propose a novel community detection algorithm that integrates team information.
To develop a multi-view network model incorporating user interactions and team structures.
To introduce a new dataset for evaluating team-aware community detection.

Main Methods:

Constructed a multi-view network with user interaction (user view) and team information (team view).
Utilized Jaccard similarity for cross-view coupling to measure consistency.
Employed multi-view modularity and the Generalized Louvain approach for optimization.

Main Results:

The proposed Team-aware Community Detection (TaCD) algorithm effectively identifies communities.
TaCD demonstrates superior performance compared to existing algorithms on benchmark datasets.
The new SCHOLAT dataset facilitates research in team-aware community detection.

Conclusions:

Integrating team information enhances community detection accuracy in social networks.
TaCD provides a robust framework for uncovering complex community structures.
The developed algorithm and dataset contribute significantly to the field of network science.