2 citations · 2 across the 3 of their papers we have counts for
4 papers
Adaptive Graph Mixture of Residual Experts: Unsupervised Learning on Diverse Graphs with Heterogeneous Specialization
Yunlong Chu, Minglai Shao, Zengyi Wo +4
Graph Neural Networks (GNNs) face a fundamental adaptability challenge: their fixed message-passing architectures struggle with the immense diversity of real-world graphs, where op…
Learning Noise-Resilient and Transferable Graph-Text Alignment via Dynamic Quality Assessment
Yuhang Liu, Minglai Shao, Zengyi Wo +5
Pre-training Graph Foundation Models (GFMs) on text-attributed graphs (TAGs) is central to web-scale applications such as search, recommendation, and knowledge discovery. However,…
Comparative Analysis Vision of Worldwide AI Courses
Jianing Xia, Man Li, Jianxin Li
This research investigates the curriculum structures of undergraduate Artificial Intelligence (AI) education across universities worldwide. By examining the curricula of leading un…
Unleashing the potential of GNNs via Bi-directional Knowledge Transfer
Shuai Zheng, Zhizhe Liu, Zhenfeng Zhu +3
Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. Howe…