TL;DR
This paper introduces two novel algorithms, RobustECD-GA and RobustECD-SE, to enhance network structures for more robust community detection, improving performance and resistance to adversarial attacks in real-world networks.
Contribution
The paper proposes two new algorithms that enhance network structure for robust community detection, addressing challenges like missing data and adversarial attacks.
Findings
Both methods outperform traditional strategies in real-world networks.
The algorithms improve robustness against adversarial attacks.
They scale effectively to large networks.
Abstract
Community detection, which focuses on clustering vertex interactions, plays a significant role in network analysis. However, it also faces numerous challenges like missing data and adversarial attack. How to further improve the performance and robustness of community detection for real-world networks has raised great concerns. In this paper, we explore robust community detection by enhancing network structure, with two generic algorithms presented: one is named robust community detection via genetic algorithm (RobustECD-GA), in which the modularity and the number of clusters are combined in a fitness function to find the optimal structure enhancement scheme; the other is called robust community detection via similarity ensemble (RobustECD-SE), integrating multiple information of community structures captured by various vertex similarities, which scales well on large-scale networks.…
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