Multi-view attributed graph clustering is an important approach to partition multi-view data based on the attribute feature and adjacent matrices from different views. Some attempts have been made in utilizing Graph Neural Network (GNN), which have achieved promising clustering performance. Despite this, few of them pay attention to the inherent specific information embedded in multiple views. Meanwhile, they are incapable of recovering the latent high-level representation from the low-level ones, greatly limiting the downstream clustering performance. To fill these gaps, a novel Dual Information enhanced multi-view Attributed Graph Clustering (DIAGC) method is proposed in this paper. Specifically, the proposed method introduces the Specific Information Reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables GCN to capture the more essential low-level representations. Besides, the Mutual Information Maximization (MIM) module maximizes the agreement between the latent high-level representation and low-level ones, and enables the high-level representation to satisfy the desired clustering structure with the help of the Self-supervised Clustering (SC) module. Extensive experiments on several real-world benchmarks demonstrate the effectiveness of the proposed DIAGC method compared with the state-of-the-art baselines.
翻译:多种观点的图表分组是基于属性特征和不同观点相邻矩阵的多视图数据分割的一个重要方法,在利用图形神经网络(GNN)方面已经作了一些尝试,取得了有希望的组合业绩;尽管如此,很少有人注意到多种观点中固有的具体信息;同时,他们无法从低层代表中恢复潜在的高级别代表,大大限制了下游分组的绩效;为填补这些差距,本文件提出了一个新的双重信息强化多视图属性图分组方法(DIAGC),具体而言,拟议方法引入了具体信息重建模块,将共识和具体信息的探索从多种观点中分离开来,使GNCN能够捕捉到更基本的低层次代表;此外,相互信息最大化模块使潜在高级别代表与低层次代表之间的协议最大化,使高级别代表能够在自上而上的组合模块的帮助下满足理想的组合结构。具体信息重建模块引入了具体信息重建模块,将共识和具体信息从多种观点中分离开来,使GNCN能够捕捉到更基本的低层次代表。此外,相互信息最大化模块使潜在高级别代表与低层次代表之间的协议最大化,并使高级别代表能够满足所期望的组合结构,并在自上超集集组群模块的帮助下满足理想的组合结构。