Deep Graph Clustering With Triple Fusion Mechanism for Community Detection

Deep graph clustering is a highly significant tool for community detection, enabling the identification of strongly connected groups of nodes within a graph. This technology is crucial in various fields such as education and E-learning. However, deep graph clustering can be more misled by the graph topology, disregarding node information. For example, an excessive number of intercommunity edges or insufficient intracommunity edges can lead to inaccurate community distinction by the model. In this article, we propose a novel model, deep Graph Clustering with Triple Fusion Autoencoder (GC-TriFA) for community detection, which utilizes a triple encoding fusion mechanism to balance the incorporation of node and topological information, thereby mitigating this issue. Specifically, GC-TriFA employs a shallow linear coding fusion and a deep coding fusion method within an autoencoder structure. This approach enables the model to simultaneously learn and capture the embedding of cross-modality information and later utilizes weight fusion to equalize the two modalities. Furthermore, GC-TriFA also reconstructs the graph structure, learns relaxed $k$-means, and undergoes self-supervised training to enhance the quality of the graph embedding. The experimental results of GC-TriFA, when evaluated as an end-to-end model on publicly available datasets, demonstrate its superiority compared to the baseline models.