18 citations · 18 across the 5 of their papers we have counts for
4 papers · 1 filter
ZeroGAR: Benchmarking the Adversarial Robustness of Zero-Shot Graph Models
Zhongjian Zhang, Xiao Wang, Busheng Zhang +5
Zero-shot graph models (ZGMs), which learn transferable knowledge from source graphs and directly apply to unseen target graphs without any adaptation, have achieved promising perf…
Relational In-Context Learning via Synthetic Pre-training with Structural Prior
Yanbo Wang, Jiaxuan You, Chuan Shi +1
Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstacle is that high-quality RDBs are…
Data-centric Federated Graph Learning with Large Language Models
Bo Yan, Zhongjian Zhang, Huabin Sun +3
In federated graph learning (FGL), a complete graph is divided into multiple subgraphs stored in each client due to privacy concerns, and all clients jointly train a global graph m…
Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
Zhongjian Zhang, Xiao Wang, Huichi Zhou +4
Graph neural networks (GNNs) are vulnerable to adversarial attacks, especially for topology perturbations, and many methods that improve the robustness of GNNs have received consid…