In recent years, self-supervised learning has emerged as a promising approach in addressing the issues of label dependency and poor generalization performance in traditional GNNs. However, existing self-supervised methods have limited effectiveness on heterophilic graphs, due to the homophily assumption that results in similar node representations for connected nodes. In this work, we propose a multi-view contrastive learning model for heterophilic graphs, namely, MUSE. Specifically, we construct two views to capture the information of the ego node and its neighborhood by GNNs enhanced with contrastive learning, respectively. Then we integrate the information from these two views to fuse the node representations. Fusion contrast is utilized to enhance the effectiveness of fused node representations. Further, considering that the influence of neighboring contextual information on information fusion may vary across different ego nodes, we employ an information fusion controller to model the diversity of node-neighborhood similarity at both the local and global levels. Finally, an alternating training scheme is adopted to ensure that unsupervised node representation learning and information fusion controller can mutually reinforce each other. We conduct extensive experiments to evaluate the performance of MUSE on 9 benchmark datasets. Our results show the effectiveness of MUSE on both node classification and clustering tasks.

我们提出了一种用于异质图的多视角对比学习模型 MUSE，通过使用增强的 GNNs 构建两个视图来捕获离心节点及其邻域的信息，并融合这两个视图的信息进行节点表示，通过融合对比增强效果，使用信息融合控制器建模本地和全局节点-邻域相似度的多样性，通过交替训练方案确保无监督节点表示学习和信息融合控制器相互增强，在9个基准数据集上实验证明了 MUSE 在节点分类和聚类任务上的有效性。

MUSE: 异质图的多视图对比学习