HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views
This work addresses limitations in graph representation learning for researchers and practitioners, though it appears incremental as it builds on existing GCL methods with novel adaptations.
The paper tackles the problem of predefined augmentations and negative sample selection in Graph Contrastive Learning (GCL) by introducing HyperGCL, a multi-modal framework that uses learnable hypergraph views, achieving state-of-the-art node classification performance on benchmark datasets.
Recent advancements in Graph Contrastive Learning (GCL) have demonstrated remarkable effectiveness in improving graph representations. However, relying on predefined augmentations (e.g., node dropping, edge perturbation, attribute masking) may result in the loss of task-relevant information and a lack of adaptability to diverse input data. Furthermore, the selection of negative samples remains rarely explored. In this paper, we introduce HyperGCL, a novel multimodal GCL framework from a hypergraph perspective. HyperGCL constructs three distinct hypergraph views by jointly utilizing the input graph's structure and attributes, enabling a comprehensive integration of multiple modalities in contrastive learning. A learnable adaptive topology augmentation technique enhances these views by preserving important relations and filtering out noise. View-specific encoders capture essential characteristics from each view, while a network-aware contrastive loss leverages the underlying topology to define positive and negative samples effectively. Extensive experiments on benchmark datasets demonstrate that HyperGCL achieves state-of-the-art node classification performance.