关键词: 关键词: 属性缺失图, 聚类, 非负矩阵分解, 图正则

Abstract: To address the inconsistency between attribute completion and clustering objectives in two-stage methods and the low efficiency of deep learning approaches for attribute-missing graph clustering, this paper proposes a stacked joint optimization-based algorithm. Firstly, a Matrix Factorization-based Attribute-Missing Graph Clustering Model (AMGC-MF) is constructed, which introduces an enhanced adjacency matrix to represent global node relations and employs graph regularization for attribute completion. Joint non-negative matrix factorization is then applied to the completed attribute matrix and the enhanced adjacency matrix to learn the cluster membership. Building on this, an Attribute-Missing Graph Clustering Model based on Stacked Joint Optimization (AMGC-SJO) is designed to dynamically update both matrices during iteration, enabling co-optimization of attribute completion and clustering. Experimental results show that AMGC-SJO outperforms AMGC-MF across multiple clustering metrics, with an average improvement of at least 0.68 percentage points on five datasets. Compared to the deep learning method AMGC, AMGC-SJO achieves comparable or better accuracy while reducing runtime by at least 89.57%. Moreover, under high missing rates (90%), AMGC-SJO demonstrates strong robustness with performance fluctuations within 4.35 percentage points, whereas AMGC-MF and AMGC decline by over 15 and 37 percentage points, respectively. The proposed AMGC-SJO offers an accurate, efficient, and robust solution for attribute-missing graph clustering, with significant theoretical and practical implications for large-scale real-world network analysis.

Key words: Keywords: attribute-missing graph, clustering, non-negative matrix factorization, graph regularization