AI RESEARCH
Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
arXiv CS.LG
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ArXi:2511.06216v2 Announce Type: replace Graph contrastive learning (GCL) learns node and graph representations by contrasting multiple views of the same graph. Existing methods typically rely on fixed, handcrafted views-usually a local and a global perspective, which limits their ability to capture multi-scale structural patterns. We present an augmentation-free, multi-view GCL framework grounded in fractional-order continuous dynamics.