In recent years, the advancement of Graph Neural Networks (GNNs) has significantly propelled progress in Multi-View Clustering (MVC). However, existing methods face the problem of coarse-grained graph fusion. Specifically, current approaches typically generate a separate graph structure for each view and then perform weighted fusion of graph structures at the view level, which is a relatively rough strategy. To address this limitation, we present a novel Mixture of Ego-Graphs Contrastive Representation Learning (MoEGCL). It mainly consists of two modules. In particular, we propose an innovative Mixture of Ego-Graphs Fusion (MoEGF), which constructs ego graphs and utilizes a Mixture-of-Experts network to implement fine-grained fusion of ego graphs at the sample level, rather than the conventional view-level fusion. Additionally, we present the Ego Graph Contrastive Learning (EGCL) module to align the fused representation with the view-specific representation. The EGCL module enhances the representation similarity of samples from the same cluster, not merely from the same sample, further boosting fine-grained graph representation. Extensive experiments demonstrate that MoEGCL achieves state-of-the-art results in deep multi-view clustering tasks. The source code is publicly available at https://github.com/HackerHyper/MoEGCL.

翻译：近年来，图神经网络的发展极大地推动了多视图聚类领域的进步。然而，现有方法面临粗粒度图融合的问题。具体而言，当前方法通常为每个视图生成独立的图结构，然后在视图层级对这些图结构进行加权融合，这种做法相对粗糙。为克服这一局限，我们提出了一种新颖的混合自我图对比表示学习方法（MoEGCL）。该方法主要由两个模块构成。首先，我们创新性地提出混合自我图融合模块（MoEGF），该模块构建自我图，并利用混合专家网络在样本层级实现自我图的细粒度融合，而非传统的视图层级融合。其次，我们引入自我图对比学习模块（EGCL），以对齐融合表示与视图特定表示。EGCL模块增强了来自同一聚类（而非仅同一样本）的样本间的表示相似性，进一步提升了图表示的细粒度。大量实验表明，MoEGCL在深度多视图聚类任务中达到了最先进水平。源代码已公开于https://github.com/HackerHyper/MoEGCL。