GRAPH NEURAL NETWORK LEARNING IN LARGE GRAPHS - A CRITICAL REVIEW
Abstract
vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff6de82b00000006ca020001000200
Graph Neural Networks have been extensively used to learn non-Euclidian structures like graphs. There have been several attempts to improve the training efficiency and to reduce the learning complexity in modelling of large graph datasets. In this paper we have reviewed the approaches which perform convolutions to model large graphs for classification and prediction. We have critically analysed each of these approaches and veracity of their claims of reduced complexity and have reported their shortcomings. We have further analysed the approaches from graph-dataset perspective.

Authors
Ashish Gavande 1, Sushil Kulkarni2
University of Mumbai, India1, University of Mumbai, India2

Keywords
Graph Neural Networks, Graph Convolutional Networks, Graph Representation Learning, Large Graph Dataset
Yearly Full Views
JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
000000000000
Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 11 , Issue: 4 , Pages: 2416-2423 )
Date of Publication :
July 2021
Page Views :
101
Full Text Views :

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.