Web: http://arxiv.org/abs/2107.04755

June 23, 2022, 1:11 a.m. | Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang

cs.LG updates on arXiv.org arxiv.org

Graph convolutional networks are becoming indispensable for deep learning
from graph-structured data. Most of the existing graph convolutional networks
share two big shortcomings. First, they are essentially low-pass filters, thus
the potentially useful middle and high frequency band of graph signals are
ignored. Second, the bandwidth of existing graph convolutional filters is
fixed. Parameters of a graph convolutional filter only transform the graph
inputs without changing the curvature of a graph convolutional filter function.
In reality, we are uncertain about …

arxiv filtering graph lg networks

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