Showing cs.LGShow all
3 papers · 1 filter
cs.LG2025
Towards Scalable and Deep Graph Neural Networks via Noise Masking
Yuxuan Liang, Wentao Zhang, Zeang Sheng +5
In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in many graph mining tasks. However, scaling them to large graphs is challenging due to the high comp…
cs.LG2024
Training-free Heterogeneous Graph Condensation via Data Selection
Yuxuan Liang, Wentao Zhang, Xinyi Gao +5
Efficient training of large-scale heterogeneous graphs is of paramount importance in real-world applications. However, existing approaches typically explore simplified models to mi…
cs.LG2024
Acceleration Algorithms in GNNs: A Survey
Lu Ma, Zeang Sheng, Xunkai Li +5
Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph-based tasks. However, their inefficiency in training and inference presents challenges for scaling up…