Simple Graph Convolutional Networks

06/10/2021
by   Luca Pasa, et al.
0

Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, that tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing simple graph convolution operators, that can be implemented in single-layer graph convolutional networks. We show that our convolution operators are more theoretically grounded than many proposals in literature, and exhibit state-of-the-art predictive performance on the considered benchmark datasets.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/23/2018

On Filter Size in Graph Convolutional Networks

Recently, many researchers have been focusing on the definition of neura...
research
06/07/2020

DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling

Graph Convolutional Networks (GCNs) have shown to be effective in handli...
research
12/19/2019

Graph Convolutional Networks: analysis, improvements and results

In the current era of neural networks and big data, higher dimensional d...
research
02/08/2022

Simplified Graph Convolution with Heterophily

Graph convolutional networks (GCNs) (Kipf Welling, 2017) attempt to ...
research
07/20/2023

QDC: Quantum Diffusion Convolution Kernels on Graphs

Graph convolutional neural networks (GCNs) operate by aggregating messag...
research
02/20/2021

Generalization bounds for graph convolutional neural networks via Rademacher complexity

This paper aims at studying the sample complexity of graph convolutional...
research
06/03/2018

Dual-Primal Graph Convolutional Networks

In recent years, there has been a surge of interest in developing deep l...

Please sign up or login with your details

Forgot password? Click here to reset