Moreover, TREE employs a co-attention mechanism, where global structural encodings of nodes, learned from network distance ... transformer-powered graph representation learning, Nature Biomedical ...

Few-shot learning addresses this by acquiring meta-knowledge. Heterogeneous graphs (HGs), rich in semantic information ... class labels and enhancing classification accuracy. Euclidean distance-based ...

Traditional deep learning (DL) methods require large amounts of labeled samples for training, making them ineffective in most practical scenarios with few-shot labeled samples. To address this ...

ICDE'25) [27/11/2024] Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning (Xinyi Gao et al. Arxiv'24) [05/09/2024] GSTAM: Efficient Graph Distillation with ...

Judea Pearl Graph Causal Learning is an emerging research area and it can be widely applied in dealing with out of distribution, fairness and explanation problems. 2025/01/24: Our survey is now ...