Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning

• URL: http://arxiv.org/abs/2606.23199v1
• Date: Mon, 22 Jun 2026 11:46:07 GMT
• Title: Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning
• Abstract summary: Attributed graph clustering partitions nodes by jointly exploiting node attributes and graph topology.<n>End-to-end framework, Any-type attributed Graph REpresentation lEarning (AGREE) is proposed.<n>It unifies attributed graphs and any-type attributed data through multi-level alignment and similarity-based graph construction.
• Score: 14.02813695997424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Attributed graph clustering partitions nodes by jointly exploiting node attributes and graph topology. It remains challenging due to attribute heterogeneity and representation degradation during graph learning. Real-world datasets often contain heterogeneous attributes, i.e., numerical and categorical attributes, complicating unified representation learning. This challenge becomes more complex in attributed graphs, where constructing a clustering-friendly graph structure from attributes and topology remains difficult. Under deep graph architectures, repeated graph propagation causes node embeddings to become overly similar, leading to the over-smoothing (OS) effect. Meanwhile, graph representation learning amplifies topological influence, making discriminative attribute information harder to exploit for clustering, an effect we refer to as over-dominating (OD). To bridge these gaps, an end-to-end framework, Any-type attributed Graph REpresentation lEarning (AGREE), is proposed. It unifies attributed graphs and any-type attributed data through multi-level alignment and similarity-based graph construction. Quaternion-based graph convolution strengthens attribute interaction to alleviate OD, while shallow graph architectures help relieve OS. The learned embeddings are jointly optimized for graph reconstruction and clustering, without requiring a predefined number of clusters during training. Experiments on diverse benchmarks show that AGREE achieves strong overall performance in accuracy, robustness, and adaptability.

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