A Framework for Accelerating Graph Convolutional Networks on Massive Datasets

Xiang Li, Ruoming Jin, Rajiv Ramnath, Gagan Agrawal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, there has been much interest in Graph Convolutional Networks (GCNs). There are several challenges associated with training GCNs. Particularly among them, because of massive scale of graphs, there is not only a large computation time, but also the need for partitioning and loading data multiple times. This paper presents a different framework in which existing GCN methods can be accelerated for execution on large graphs. Building on top of ideas from meta-learning we present an optimization strategy. This strategy is applied to three existing frameworks, resulting in new methods that we refer to as GraphSage++, ClusterGCN++, and GraphSaint++. Using graphs with order of 100 million edges, we demonstrate that we reduce the overall training time by up to 30%, while not having a noticeable reduction in F1 scores in most cases.

Original languageEnglish (US)
Title of host publicationComputational Data and Social Networks - 10th International Conference, CSoNet 2021, Proceedings
EditorsDavid Mohaisen, Ruoming Jin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages79-92
Number of pages14
ISBN (Print)9783030914332
DOIs
StatePublished - 2021
Event10th International Conference on Computational Data and Social Networks, CSoNet 2021 - Virtual Online
Duration: Nov 15 2021Nov 17 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13116 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Computational Data and Social Networks, CSoNet 2021
CityVirtual Online
Period11/15/2111/17/21

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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