2 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
Can Directed Graph Neural Networks be Adversarially Robust?
Zhichao Hou, Xitong Zhang, Wei Wang +2
The existing research on robust Graph Neural Networks (GNNs) fails to acknowledge the significance of directed graphs in providing rich information about networks' inherent structu…
cs.LG2023★ 1 cited
Towards Label Position Bias in Graph Neural Networks
Haoyu Han, Xiaorui Liu, Feng Shi +3
Graph Neural Networks (GNNs) have emerged as a powerful tool for semi-supervised node classification tasks. However, recent studies have revealed various biases in GNNs stemming fr…
cs.LG2023★ 2 cited
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
Rui Xue, Haoyu Han, MohamadAli Torkamani +2
Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs). But deeper GNNs suffer from the long-lasting sca…