A Simple Yet Effective SVD-GCN for Directed Graphs

TMLR Paper116 Authors

Abstract: In this paper, we will present a simple yet effective way for directed Graph (digraph) Convolutional Neural Networks based on the classic Singular Value Decomposition (SVD), named SVD-GCN for digraphs. Through empirical experiments on node classification datasets, we have found that SVD-GCN has remarkable improvements in a number of graph node learning tasks and outperforms GCN and many other state-of-the-art graph neural networks.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission: All the major changes are highlighted while minor changes are not highlighted

Assigned Action Editor: ~Manzil_Zaheer1

Submission Number: 116