Graph clustering method improves results despite missing data
Robust Graph Clustering Network for Multiple Missing Data
Machine Learning
Summary
Clustering means grouping things that are similar, but it gets tricky when some information about the items or their connections is missing. The authors developed a new method that better guesses missing details and keeps groups clear and separated. Their approach uses two separate steps to recover missing data, a special way to keep groups tight and distinct, and a technique to avoid confusion at group edges. Tests show their method works better than existing ones on real messy data.
What this means in practice
- •For data engineers: Improve clustering quality on incomplete graph datasets common in social networks or sensor networks despite missing nodes or connections.
- •For machine learning engineers: Enhance model robustness when training clustering algorithms on multi-view graph data with partial attribute and structure loss.
Authors
Keyuan Qiu, Renda Han, Zhen Tang, Qiang He, Xingwei Wang, Wenxin Zhang, Guangzhen Yao, Junxin Chen, Qingjian Ni
Abstract
Clustering on graphs where both node attributes and structural links are partially missing remains a challenging task. Existing methods typically rely on imputation-then-clustering on single-view missingness incomplete graphs, which are vulnerable to cross-view error propagation and cluster-boundary blurring under simultaneous attribute and structure missingness. To address these limitations, we propose a Robust Graph Clustering Network for Multiple Missing Data (RGCN), which is designed to handle simultaneous node attribute and graph structure incompleteness. RGCN introduces three key innovations: First, we design a view-decoupled dual-branch imputation to mitigate interference and enable mutual enhancement in recovering missing data. Second, we employ a multi-hyperspherical mixture prior to enhance intra-cluster compactness and inter-cluster separability on a directional latent manifold. Third, a boundary-aware contrastive enhancement objective mitigates the blurring of clusters caused by imputation bias. Extensive experiments on real-world datasets demonstrate that RGCN consistently outperforms state-of-the-art baselines under various missing patterns.