Learning Graph While Training: An Evolving Graph Convolutional Neural Network

08/10/2017
by   Ruoyu Li, et al.
0

Convolution Neural Networks on Graphs are important generalization and extension of classical CNNs. While previous works generally assumed that the graph structures of samples are regular with unified dimensions, in many applications, they are highly diverse or even not well defined. Under some circumstances, e.g. chemical molecular data, clustering or coarsening for simplifying the graphs is hard to be justified chemically. In this paper, we propose a more general and flexible graph convolution network (EGCN) fed by batch of arbitrarily shaped data together with their evolving graph Laplacians trained in supervised fashion. Extensive experiments have been conducted to demonstrate the superior performance in terms of both the acceleration of parameter fitting and the significantly improved prediction accuracy on multiple graph-structured datasets.

READ FULL TEXT

page 5

page 6

research
01/10/2018

Adaptive Graph Convolutional Neural Networks

Graph Convolutional Neural Networks (Graph CNNs) are generalizations of ...
research
03/27/2018

Tensor graph convolutional neural network

In this paper, we propose a novel tensor graph convolutional neural netw...
research
02/14/2018

Edge Attention-based Multi-Relational Graph Convolutional Networks

Graph convolutional network (GCN) is generalization of convolutional neu...
research
05/11/2021

Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity

Rationalizing which parts of a molecule drive the predictions of a molec...
research
04/26/2017

A Generalization of Convolutional Neural Networks to Graph-Structured Data

This paper introduces a generalization of Convolutional Neural Networks ...
research
07/07/2022

Machine learning of percolation models using graph convolutional neural networks

Percolation is an important topic in climate, physics, materials science...
research
08/24/2018

Future Automation Engineering using Structural Graph Convolutional Neural Networks

The digitalization of automation engineering generates large quantities ...

Please sign up or login with your details

Forgot password? Click here to reset