32 citations · 139 across the 14 of their papers we have counts for
15 papers · 1 filter
DGI: Easy and Efficient Inference for GNNs
Peiqi Yin, Xiao Yan, Jinjing Zhou +5
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model inference and evaluation remain to be addressed. For instance, using the widely adopte…
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
Yongqiang Chen, Han Yang, Yonggang Zhang +4
Recently Graph Injection Attack (GIA) emerges as a practical attack scenario on Graph Neural Networks (GNNs), where the adversary can merely inject few malicious nodes instead of m…
Rethinking Graph Regularization for Graph Neural Networks
Han Yang, Kaili Ma, James Cheng
The graph Laplacian regularization term is usually used in semi-supervised representation learning to provide graph structure information for a model . However, with the rece…
Understanding Graph Neural Networks from Graph Signal Denoising Perspectives
Guoji Fu, Yifan Hou, Jian Zhang +3
Graph neural networks (GNNs) have attracted much attention because of their excellent performance on tasks such as node classification. However, there is inadequate understanding o…
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates
Kaiwen Zhou, Anthony Man-Cho So, James Cheng
We propose a new methodology to design first-order methods for unconstrained strongly convex problems. Specifically, instead of tackling the original objective directly, we constru…
Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs
Han Yang, Xiao Yan, Xinyan Dai +2
Graph neural networks (GNNs) have received much attention recently because of their excellent performance on graph-based tasks. However, existing research on GNNs focuses on design…