  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.
