Revisit the Scalability of Deep Auto-Regressive Models for Graph Generation

Shuai Yang, Xipeng Shen, Seung Hwan Lim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As a new promising approach to graph generations, deep auto-regressive graph generation has drawn increasing attention. It however has been commonly deemed as hard to scale up to work with large graphs. In existing studies, it is perceived that the consideration of the full non-local graph dependences is indispensable for this approach to work, which entails the needs for keeping the entire graph's info in memory and hence the perceived 'inherent' scalability limitation of the approach. This paper revisits the common perception. It proposes three ways to relax the dependences and conducts a series of empirical measurements. It concludes that the perceived 'inherent' scalability limitation is a misperception; with the right design and implementation, deep auto-regressive graph generation can be applied to graphs much larger than the device memory. The rectified perception removes a fundamental barrier for this approach to meet practical needs.

Original languageEnglish
Title of host publicationIJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738133669
DOIs
StatePublished - Jul 18 2021
Event2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China
Duration: Jul 18 2021Jul 22 2021

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2021-July

Conference

Conference2021 International Joint Conference on Neural Networks, IJCNN 2021
Country/TerritoryChina
CityVirtual, Shenzhen
Period07/18/2107/22/21

Funding

Acknowledgement This material is based upon work supported by the National Science Foundation (NSF) under Grants CCF-1703487. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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