10 citations · 11 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 10 cited
Improving the Expressiveness of -hop Message-Passing GNNs by Injecting Contextualized Substructure Information
Tianjun Yao, Yiongxu Wang, Kun Zhang +1
Graph neural networks (GNNs) have become the \textit{de facto} standard for representational learning in graphs, and have achieved state-of-the-art performance in many graph-relate…
cs.LG2023★ 1 cited
Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks (Student Abstract)
Huixin Zhan, Kun Zhang, Keyi Lu +1
In this paper, we measure the privacy leakage via studying whether graph representations can be inverted to recover the graph used to generate them via graph reconstruction attack…