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Hongda Qi

2 accepted papers

2025

Wave-driven Graph Neural Networks with Energy Dynamics for Over-smoothing Mitigation

IJCAI 2025

Over-smoothing is a persistent challenge in Graph Neural Networks (GNNs), where node embeddings become indistinguishable as network depth increases, fundamentally limiting their effectiveness on tasks requiring fine-grained distinctions. This issue arises from the reliance on diffusion-based propaga

2024

Unifying Homophily and Heterophily for Spectral Graph Neural Networks via Triple Filter Ensembles

NeurIPS 2024poster

Polynomial-based learnable spectral graph neural networks (GNNs) utilize polynomial to approximate graph convolutions and have achieved impressive performance on graphs. Nevertheless, there are three progressive problems to be solved. Some models use polynomials with better approximation for approxi…

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