Beyond low-pass filtering on large-scale graphs via Adaptive Filtering Graph Neural Networks

Author:

Zhang QiORCID,Li Jinghua,Sun Yanfeng,Wang ShaofanORCID,Gao JunbinORCID,Yin Baocai

Funder

National Natural Science Foundation of China

Natural Science Foundation of Beijing Municipality

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference56 articles.

1. Bridging the gap between spectral and spatial domains in graph neural networks;Balcilar,2020

2. Beyond low-frequency information in graph convolutional networks;Bo,2021

3. A note on over-smoothing for graph neural networks;Cai,2020

4. Chen, J., Ma, T., & Xiao, C. (2018). FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In Proceedings of the international conference on learning representations.

5. Discrete signal processing on graphs: Sampling theory;Chen;IEEE Transactions on Signal Processing,2015

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