Contrastive Learning for Non-Local Graphs with Multi-Resolution
  Structural Views
Asif Khan Amos Storkey
Learning node-level representations of heterophilic graphs is crucial for
various applications, including fraudster detection and protein function
prediction. In such graphs, nodes share structural similarity identified by the
equivalence of their connectivity which is implicitly encoded in the form of
higher-order hierarchical information in the graphs. The contrastive methods
are popular choices for learning the representation of nodes in a graph.
However, existing contrastive methods struggle to capture higher-order graph
structures. To address this limitation, we propose a novel multiview
contrastive learning approach that integrates diffusion filters on graphs. By
incorporating multiple graph views as augmentations, our method captures the
structural equivalence in heterophilic graphs, enabling the discovery of hidden
relationships and similarities not apparent in traditional node
representations. Our approach outperforms baselines on synthetic and real
structural datasets, surpassing the best baseline by $16.06\%$ on Cornell,
$3.27\%$ on Texas, and $8.04\%$ on Wisconsin. Additionally, it consistently
achieves superior performance on proximal tasks, demonstrating its
effectiveness in uncovering structural information and improving downstream
applications.