Centrality-Aware Collaborative Network Embedding for Overlapping Community Detection
Xi Cheng, Wenjie Zhu, Wei Qi Yan
Abstract
Community detection is crucial for uncovering the intricate structures and dynamics within various networks, ranging from social interactions to biological systems. Despite significant advancements in community detection approaches, the task of learning network embeddings for complex networks characterized by overlapping communities presents considerable challenges. Specifically, nodes with higher centrality tend to contribute to increased overlaps across multiple communities, complicating the network embedding process. In this paper, we propose a Centrality-Aware Collaborative Learning (CACL) framework that leverages higher-order information through collaborative learning, incorporating a centrality penalty for overlapping community detection. The CACL framework integrates symmetric non-negative matrix factorization and kernel regression models, effectively addressing the limitations associated with traditional single-model techniques. Additionally, it emphasizes the contributions of nodes with higher centrality in the collaborative paradigm. By employing both first-order and second-order information, CACL preserves the intrinsic structure of the network, allowing for a more comprehensive representation of community relationships. To optimize the learning process of CACL, we implement a coordinate descent optimization scheme that tailors network embeddings specifically for overlapping community detection, thereby avoiding ad-hoc processing methods. Extensive experiments on ten LFR benchmark networks and six real-world networks demonstrate that CACL outperforms competing methods in accurately identifying overlapping communities, highlighting its efficacy in centrality-aware collaborative learning.