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arXiv AI By Wei Ju, Siyu Yi, Kangjie Zheng, Yifan Wang, Ziyue Qiao, Li Shen, Yongdao Zhou, Xiaochun Cao, Jiancheng Lv

Compactness and Consistency: A Conjoint Framework for Deep Graph Clustering

Read the original on arXiv AI →

The paper introduces CoCo, a conjoint framework for deep graph clustering that emphasizes compactness and consistency in node representations. CoCo uses graph convolutional filters to learn robust embeddings from both local and global views, then encodes them into low‑rank compact embeddings to reduce redundancy and noise. A consistency learning strategy further enriches node semantics, enabling knowledge transfer between perspectives and yielding superior performance over state‑of‑the‑art methods on multiple datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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