21 citations · 47 across the 7 of their papers we have counts for
5 papers · 1 filter
Un-mixing Test-time Adaptation under Heterogeneous Data Streams
Zixian Su, Jingwei Guo, Xi Yang +2
Deploying deep models in real-world scenarios remains challenging due to significant performance drops under distribution shifts between training and deployment environments. Test-…
Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filtering
Jingwei Guo, Kaizhu Huang, Xinping Yi +2
Whilst spectral Graph Neural Networks (GNNs) are theoretically well-founded in the spectral domain, their practical reliance on polynomial approximation implies a profound linkage…
Graph Neural Networks with Diverse Spectral Filtering
Jingwei Guo, Kaizhu Huang, Xinping Yi +1
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…
ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting
Jingwei Guo, Kaizhu Huang, Rui Zhang +1
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. However…
LGD-GCN: Local and Global Disentangled Graph Convolutional Networks
Jingwei Guo, Kaizhu Huang, Xinping Yi +1
Disentangled Graph Convolutional Network (DisenGCN) is an encouraging framework to disentangle the latent factors arising in a real-world graph. However, it relies on disentangling…