FD-GCL is an augmentation-free, negative-free graph contrastive learner that uses two fractional-diffusion encoders with different orders to generate local and global views, reporting state-of-the-art node classification on several heterophilic benchmarks.
HGRL relies on graph augmentations, while DSSL assumes a graph generation process, which may not always reflect real-world graphs
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Simple Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
FD-GCL is an augmentation-free, negative-free graph contrastive learner that uses two fractional-diffusion encoders with different orders to generate local and global views, reporting state-of-the-art node classification on several heterophilic benchmarks.