Graph Neural Networks Enhance Network Clustering with Self-Learning

By PulseAugur Editorial · [1 sources] · 2026-06-11 15:05

Researchers have developed a novel framework for graph clustering that leverages Graph Neural Networks (GNNs) and a self-learning approach. This method iteratively refines node representations by using GNNs to cluster nodes, then updating the graph based on these clusters for the next round of representation generation. The framework also incorporates a context graph to enhance node representations. Empirical results demonstrate its effectiveness in extracting information from both network edges and node attributes, outperforming methods that focus on only one aspect, particularly when attributes are not highly informative. The iterative learning process also shows superior performance compared to a single training round. AI

RANK_REASON This is a research paper detailing a novel methodology for graph clustering using GNNs and self-learning. [lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Daniel Ratton Figueiredo · 2026-06-11 15:05

Clustering Node Attributed Networks with Graph Neural Networks and Self Learning

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 communit…

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Clustering Node Attributed Networks with Graph Neural Networks and Self Learning

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