Research map / Graph learning / Graph self-supervised learning
Adaptive Multi-view Graph Contrastive Learning via Fractional Continuous Dynamics
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Proposes an augmentation-free, adaptive multi-view graph contrastive learning framework that generates views from fractional-order continuous dynamics on graphs.
Problem
Existing graph contrastive learning methods rely on a small set of fixed, handcrafted views (typically local and global), which limits their ability to capture multi-scale structural patterns.
Approach
Replaces fixed handcrafted augmentations with multiple adaptive views derived from fractional-order continuous dynamics, enabling augmentation-free multi-scale contrastive learning.
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