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AGRENET: Attributed graph rotation embedding network for clustering
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AI
DOI：10.1016/j.engappai.2026.113758.png)
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Abstract
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Recent studies leverage graph neural networks (GNNs) to learn node embeddings, subsequently employing conventional clustering techniques to identify clusters. However, these methods face several challenges. Firstly, the use of the original graph structure is not ideal for clustering as it is often plagued by noise and sparsity issues. Additionally, non-clustering driven losses are typically used, which might fail to represent the overall cluster structure. Consequently, the generated embeddings are often insufficient for the subsequent clustering task. To tackle these challenges, we introduce an innovative framework, the attributed graph rotation embedding network for clustering (AGRENET), which leverages full graph information based on the proposed dynamic global attention to improve graph structure. The proposed framework learns node embeddings through a spectral rotation embedding loss that integrates both feature and structure information into a kernel order learning via a higher-order graph convolution. Combining graph structure improvement, embedding network construction, and kernel order learning, AGRENET allows the improved graph structure to encode third-order proximities, thus reducing noise and sparsity issues. Experiments on four real-world and two synthetic attributed graph datasets demonstrate that AGRENET consistently outperforms state-of-the-art methods. Specifically, it achieves an average improvement of 11.5% in Accuracy, 17.3% in Normalized Mutual Information, 17.3% in Adjusted Rand Index, and 13.8% in F1-score over the best baseline. The results of experiments conducted on six benchmark graphs show that AGRENET surpasses cutting-edge methods with respect to clustering performance. The code is available at: https://github.com/YingXie/AGRENET .
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