no code implementations • 12 Nov 2021 • Yongyi Yang, Tang Liu, Yangkun Wang, Zengfeng Huang, David Wipf
It has been observed that graph neural networks (GNN) sometimes struggle to maintain a healthy balance between the efficient modeling long-range dependencies across nodes while avoiding unintended consequences such oversmoothed node representations or sensitivity to spurious edges.
1 code implementation • 9 Jun 2021 • Zengfeng Huang, Shengzhong Zhang, Chong Xi, Tang Liu, Min Zhou
Scalability of graph neural networks remains one of the major challenges in graph machine learning.
1 code implementation • 10 Mar 2021 • Yongyi Yang, Tang Liu, Yangkun Wang, Jinjing Zhou, Quan Gan, Zhewei Wei, Zheng Zhang, Zengfeng Huang, David Wipf
Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to oversmoothing, long-range dependencies, and spurious edges, e. g., as can occur as a result of graph heterophily or adversarial attacks.