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Self-supervised graph autoencoder with redundancy reduction for community detection
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AI
DOI：10.1016/j.neucom.2024.127703.png)
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Abstract
En 中文
Community detection is a significant research topic in network science, which has been revisited with graph neural networks. As a powerful graph representation learning model, graph autoencoder (GAE) is commonly used for unsupervised community detection. However, most GAE-based methods ignore multi -scale features of encoding layers, which inherently provide useful information for community detection. Moreover, these methods fail to simultaneously improve the representation learning process and clustering performance through a unified objective function. To address these issues, we propose a self -supervised graph autoencoder model with redundancy reduction for community detection. Firstly, we introduce a multi -scale module based on GAE to obtain discriminative node representations from different encoding layers. In particular, a redundancy reduction strategy is employed to eliminate redundancy information in the latent embedding space. Then, a node clustering module is used to obtain initial community labels. To fully utilize the multi -scale features to further refine clustering performance, a self -supervised module is designed to utilize current clustering labels to supervise the node representation learning process, thus constructing an end -to -end model for community detection. Finally, we validate the effectiveness of the proposed method on real -world networks. Experimental results demonstrate that our method outperforms several state-of-the-art methods in community detection.
Keywords:
Graph autoencoder
Community detection
Multi-scale features
Self-supervised learning
Redundancy reduction