no code implementations • 17 Jan 2024 • Jingwei Guo, Kaizhu Huang, Xinping Yi, Zixian Su, Rui Zhang
Whilst spectral Graph Neural Networks (GNNs) are theoretically well-founded in the spectral domain, their practical reliance on polynomial approximation implies a profound linkage to the spatial domain.
1 code implementation • 15 Dec 2023 • Zixian Su, Jingwei Guo, Kai Yao, Xi Yang, Qiufeng Wang, Kaizhu Huang
While recent test-time adaptations exhibit efficacy by adjusting batch normalization to narrow domain disparities, their effectiveness diminishes with realistic mini-batches due to inaccurate target estimation.
1 code implementation • 14 Dec 2023 • Jingwei Guo, Kaizhu Huang, Xinping Yi, Rui Zhang
Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph machine learning, with polynomial filters applied for graph convolutions, where all nodes share the identical filter weights to mine their local contexts.
1 code implementation • 27 May 2022 • Jingwei Guo, Kaizhu Huang, Rui Zhang, Xinping Yi
While Graph Neural Networks (GNNs) have achieved enormous success in multiple graph analytical tasks, modern variants mostly rely on the strong inductive bias of homophily.
1 code implementation • 24 Apr 2021 • Jingwei Guo, Kaizhu Huang, Xinping Yi, Rui Zhang
}, we introduce a novel Local and Global Disentangled Graph Convolutional Network (LGD-GCN) to capture both local and global information for graph disentanglement.