Graph Prompt Learning (GPL) bridges significant disparities between pretraining and
downstream applications to alleviate the knowledge transfer bottleneck in real-world graph …

Abstract Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph
representation learning method. However, it has been shown that GCL is vulnerable to …

Recently, graph condensation has emerged as a prevalent technique to improve the training
efficiency for graph neural networks (GNNs). It condenses a large graph into a small one …

Backdoor attacks in the traditional graph neural networks (GNNs) field are easily detectable
due to the dilemma of confusing labels. To explore the backdoor vulnerability of GNNs and …

Graph neural networks (GNNs) have emerged as a powerful model to capture critical graph
patterns. Instead of treating them as black boxes in an end-to-end fashion, attempts are …

Graph Neural Networks (GNNs) have been shown to be susceptible to backdoor poisoning
attacks, which pose serious threats to real-world applications. Meanwhile, graph reduction …

Graph Prompt Learning (GPL) has been introduced as a promising approach that uses
prompts to adapt pre-trained GNN models to specific downstream tasks without requiring …

Recent studies have revealed that GNNs are highly susceptible to multiple adversarial
attacks. Among these, graph backdoor attacks pose one of the most prominent threats …

Graph self-supervised learning is now a go-to method for pre-training graph foundation
models, including graph neural networks, graph transformers, and more recent large …

Graph prompt tuning has emerged as a promising paradigm to effectively transfer general
graph knowledge from pre-trained models to various downstream tasks, particularly in few …