A Simple Yet Effective SVD-GCN for Directed Graphs. (arXiv:2205.09335v1 [cs.LG])

In this paper, we propose a simple yet effective graph neural network for
directed graphs (digraph) based on the classic Singular Value Decomposition
(SVD), named SVD-GCN. The new graph neural network is built upon the graph
SVD-framelet to better decompose graph signals on the SVD ``frequency'' bands.
Further the new framelet SVD-GCN is also scaled up for larger scale graphs via
using Chebyshev polynomial approximation. Through empirical experiments
conducted on several node classification datasets, we have found that SVD-GCN
has …

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