11 papers
Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts
Quanxin Wang, Xuanting Xie, Bingheng Li +4
Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts. However, existing methods operate within a…
Full spectrum Unlearnable Examples via Spectral Equalization
Jiale Cai, Gezheng Xu, Zhihao Li +6
Unlearnable examples (UEs) protect training data by injecting imperceptible perturbations so that models fail to extract exploitable representations. In this paper, we reveal that…
The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning
Shuo Wang, Xiangyu Wang, Quanxin Wang +9
Current evaluation practices in relational learning rely heavily on flat leaderboards that average performance across heterogeneous datasets, implicitly assuming a uniform underlyi…
Scaling-Aware Adapter for Structure-Grounded LLM Reasoning
Zihao Jing, Qiuhao Zeng, Ruiyi Fang +4
Large language models (LLMs) are enabling reasoning over 2D and 3D structures, yet existing methods remain modality-specific and typically compress structural inputs through sequen…
Structure-Centric Graph Foundation Model via Geometric Bases
Xiaodong He, Haolan He, Ruiyi Fang +2
Graph foundation models (GFMs) seek transferable representations across graph domains but are limited by structural heterogeneity and incompatible node feature spaces. We propose S…
When Priors Backfire: On the Vulnerability of Unlearnable Examples to Pretraining
Zhihao Li, Gezheng Xu, Jiale Cai +5
Unlearnable Examples (UEs) serve as a data protection strategy that generates imperceptible perturbations to mislead models into learning spurious correlations instead of underlyin…