Noised Multi-layer Networks Clustering with Graph Denoising and Structure Learning

Multi-layer networks treat various types of interactions at each level to model complex systems in nature and society, and clustering of them is of great significance for revealing mechanisms of systems. Vast majority of current algorithms focus on identifying the common communities in clear multi-layer networks, and few attempt has been devoted to the detection of layer-specific communities in noised ones. To address these issues, a joint learning algorithm with G raph D enoising and S tructure L earning (called GDSL ) for the detection of layer-specific communities in noised multi-layer networks is proposed, which simultaneously integrates graph denoising, structure learning, and module detection. To remove noise of networks, GDSL re-constructs affinity graphs for the original ones by preserving community structure. To enhance robustness and discriminative of features, GDSL explores the relations of features among various layers with the Hilbert-Schmidt Independence Criterion and structure learning. Finally, GDSL joins all these procedures with an objective function, and deduces optimization rules. The results show that GDSL not only significantly outperforms baselines but also enhances the robustness of the algorithm, providing an effective model for community detection in noised multi-layer networks.