PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection

no code implementations • 6 Feb 2024 • Enyan Dai, Minhua Lin, Suhang Wang

PreGIP incorporates a task-free watermarking loss to watermark the embedding space of pretrained GNN encoder.

Certifiably Robust Graph Contrastive Learning

1 code implementation • NeurIPS 2023 • Minhua Lin, Teng Xiao, Enyan Dai, Xiang Zhang, Suhang Wang

Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed method in providing effective certifiable robustness and enhancing the robustness of any GCL model.

Contrastive Learning Graph Representation Learning

Unnoticeable Backdoor Attacks on Graph Neural Networks

1 code implementation • 11 Feb 2023 • Enyan Dai, Minhua Lin, Xiang Zhang, Suhang Wang

In particular, backdoor attack poisons the graph by attaching triggers and the target class label to a set of nodes in the training graph.

Backdoor Attack Graph Classification +1

Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function

1 code implementation • 1 Sep 2020 • Binghui Wang, Tianxiang Zhou, Minhua Lin, Pan Zhou, Ang Li, Meng Pang, Hai Li, Yiran Chen

Specifically, we first introduce two influence functions, i. e., feature-label influence and label influence, that are defined on GNNs and label propagation (LP), respectively.

Node Classification