DeepAI
Log In Sign Up

Sampling methods for efficient training of graph convolutional networks: A survey

03/10/2021
by   Xin Liu, et al.
0

Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GCN performs well compared with other methods, it still faces challenges. Training a GCN model for large-scale graphs in a conventional way requires high computation and memory costs. Therefore, motivated by an urgent need in terms of efficiency and scalability in training GCN, sampling methods are proposed and achieve a significant effect. In this paper, we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN. To highlight the characteristics and differences of sampling methods, we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories. Finally, we discuss some challenges and future research directions of the sampling methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

01/30/2018

FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

The graph convolutional networks (GCN) recently proposed by Kipf and Wel...
07/12/2022

From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have been shown to be a powerful con...
08/22/2019

Spam Review Detection with Graph Convolutional Networks

Customers make a lot of reviews on online shopping websites every day, e...
07/10/2019

GraphSAINT: Graph Sampling Based Inductive Learning Method

Graph Convolutional Networks (GCNs) are powerful models for learning rep...
11/01/2021

GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing

Recently, Graph Convolutional Networks (GCNs) have become state-of-the-a...