IEEE Access (Jan 2024)
GCN-Based Encoder-Decoder Networks With Markov Random Field for Unsupervised Community Detection
Abstract
Community discovery is an essential research area with significant real-world applications. Lately, Graph Convolutional Networks (GCNs) have gained popularity for their ability to effectively identify and represent node relationships in low-dimensional spaces, enhancing community detection. However, previous studies have ignored two problems: insufficient network node labeling and incomplete information capture of similar dependencies between nodes, which affect the understanding and analysis of the network and the accuracy of node embedding. To this end, this paper proposed GCN-based encoder-decoder networks with Markov random field for unsupervised community detection. Specifically, we propose a comprehensive framework for enhancing the representation of learning nodes, which consists of the following components: a GCN-based encoder-decoder module, an MRF-based information enhancement module, and a modularity maximization module. The GCN-based encoder-decoder module uses graph attention autoencoder and dual decoder to learn node embedding matrix, which solves the problems of insufficient node labels and unclear information. The MRF-based information enhancement module uses the Markov random field to comprehensively consider the characteristics of the node itself and the relationship between the nodes to improve the representation ability of node embedding and solves the problem of incomplete information acquisition of similar dependencies between nodes. The modularity maximization module divides the network into subgroups with tight connections and divides similar nodes into the same community. Finally, we conducted node clustering experiments on three datasets, which showed improvements of approximately 0.3 percent, 3 percent, and 5 percent compared to the state-of-the-art baseline, respectively.
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