2 papers
cs.LG2025
Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning
Chu-Yuan Wei, Shun-Yao Liu, Sheng-Da Zhuo +3
Graph Neural Networks (GNNs) have achieved remarkable success in various graph-based tasks (e.g., node classification or link prediction). Despite their triumphs, GNNs still face c…
cs.LG2025
EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning
Shengda Zhuo, Jiwang Fang, Hongguang Lin +4
Graph Neural Networks (GNNs) have significant advantages in handling non-Euclidean data and have been widely applied across various areas, thus receiving increasing attention in re…