Clustering Node Attributed Networks with Graph Neural Networks and Self Learning

• URL: http://arxiv.org/abs/2606.13444v1
• Date: Thu, 11 Jun 2026 15:05:33 GMT
• Title: Clustering Node Attributed Networks with Graph Neural Networks and Self Learning
• Abstract summary: This work proposes a novel framework that builds on prior works that have applied graph neural networks (GNN) to graph clustering.<n>In each round, a GNN generates representations for nodes that are used to cluster the nodes.<n>This clustering influences the graph used to generate the node representation in the next round.<n> Empirical results show that the proposed methodology extracts information from both network edges and node attributes in synthetic data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph clustering - partitioning the node set of a graph into disjoint subsets that reflect some latent information - is a fundamental problem as it finds applications in a myriad of different scenarios. While this classic problem has been tackled for decades by different communities, a recent variation of the problem driven by real data considers the scenario where nodes have attributes that are also informative. This has triggered novel methods that simultaneously leverage network information (edges) and node information (attributed) in the design of novel clustering algorithms. This work proposes a novel framework that builds on prior works that have applied graph neural networks (GNN) to graph clustering. The proposed framework operates in rounds of self learning in a fully unsupervised setting. In each round, a GNN generates representations for nodes that are used to cluster the nodes. This clustering influences the graph used to generate the node representation in the next round. Moreover, a context graph built in each round using the original graph is used to generate the node representations. Empirical results show that the proposed methodology extracts information from both network edges and node attributes in synthetic data, outperforming algorithms focused solely on the network or attributes when neither are very informative. Multiple rounds of learning also improve the performance and always outperforms a long single round of training (i.e., classic GNN graph clustering). When considering real datasets, empirical results indicate that the proposed methodology is competitive to state-of-the-art methods when cluster sizes are balanced.

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