When graph neural networks meet deep nonnegative matrix factorization: An encoder and decoder-like method for community detection

Community detection is the best choice for accomplishing a variety of tasks in networks. It aims to find the optimal cluster structures, i.e., the high correlation between nodes in intra-cluster and low correlation between them in different clusters. Deep Nonnegative Matrix Factorization (DNMF) and...

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Vydané v:	Expert systems with applications Ročník 271; s. 126676
Hlavní autori:	Cheng, Junwei, He, Chaobo, Lin, Xuequan, Liu, Weixiong, Han, Kunlin, Tang, Yong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.05.2025
Predmet:	Autoencoder Community detection Deep nonnegative matrix factorization Graph neural networks Deep nonnegative matrix factorization Community detection Graph neural networks Autoencoder
ISSN:	0957-4174
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Popis
Shrnutí:	Community detection is the best choice for accomplishing a variety of tasks in networks. It aims to find the optimal cluster structures, i.e., the high correlation between nodes in intra-cluster and low correlation between them in different clusters. Deep Nonnegative Matrix Factorization (DNMF) and Graph Neural Networks (GNNs) have attracted much attention because of their advantages. And they have been widely adopted for community detection. Unfortunately, the performance of these methods is limited by their respective shortcomings. Therefore, their performance is distant from satisfactory. Especially, the innate feature of DNMF makes it difficult to cope with complex networks and withstand topology noise. Additionally, the performance of GNNs is limited by over-smoothing problem. To overcome the aforementioned flaws, we propose a novel method, SADNG, which connects the DNMF and GNNs seamlessly. Specifically, the over-smoothing problem of GNNs is alleviated when DNMF participates in convolution operations. And the abilities of DNMF to withstand topology noise and to reconstruct non-linear networks are boosted when GNNs participates in reconstructing weighted networks. In all, this way of cooperation compensates for their respective disadvantages. Experimental results across multiple benchmark datasets, including Cora and Citeseer, demonstrate that SADNG is superior to most state-of-the-art methods in community detection. •Integrates GCN and DNMF for enhanced community detection.•Addresses topology noise in dense networks effectively.•Mitigates over-smoothing in GCN using hierarchical mappings.•Outperforms state-of-the-art methods across multiple datasets.•Provides robust results in networks with hub nodes.
ISSN:	0957-4174
DOI:	10.1016/j.eswa.2025.126676