Wenning Wu — accepted AI-conference papers

2026

Source-Free Graph Foundation Model Adaptation via Pseudo-Source Reconstruction

Liang Yang, Hui Ning, Jiaming Zhuo, Ziyi Ma, Chuan Wang, Wenning Wu +1

AAAI 2026technical

Aiming to overcome distribution shift and label sparsity that hinder cross-domain generalization of Graph Neural Networks (GNNs), Unsupervised Graph Domain Adaptation (UGDA) transfers knowledge from a label-rich source to an unlabeled target graph. Yet in practice, strict privacy protocols often wit

Cited by 0 Source ↗PDF ↗Scholar ↗

2025

A Closer Look at Graph Transformers: Cross-Aggregation and Beyond

Jiaming Zhuo, Ziyi Ma, Yintong Lu, Yuwei Liu, Kun Fu, Di Jin +5

NeurIPS 2025spotlight

Graph Transformers (GTs), which effectively capture long-range dependencies and structural biases simultaneously, have recently emerged as promising alternatives to traditional Graph Neural Networks (GNNs). Advanced approaches for GTs to leverage topology information involve integrating GNN modules…

Cited by 0 Source ↗Scholar ↗