short-paper

GraphPAS: Parallel Architecture Search for Graph Neural Networks

Authors:

Moctard Babatounde Oloulade,

Zhao LiAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2182 - 2186

https://doi.org/10.1145/3404835.3463007

Published: 11 July 2021 Publication History

Abstract

Graph neural architecture search has received a lot of attention as Graph Neural Networks (GNNs) has been successfully applied on the non-Euclidean data recently. However, exploring all possible GNNs architectures in the huge search space is too time-consuming or impossible for big graph data. In this paper, we propose a parallel graph architecture search (GraphPAS) framework for graph neural networks. In GraphPAS, we explore the search space in parallel by designing a sharing-based evolution learning, which can improve the search efficiency without losing the accuracy. Additionally, architecture information entropy is adopted dynamically for mutation selection probability, which can reduce space exploration. The experimental result shows that GraphPAS outperforms state-of-art models with efficiency and accuracy simultaneously.

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Cited By

He CChen JLi QWang YGao J(2025)Decoupled graph neural architecture search with explainable variable propagation operationKnowledge and Information Systems10.1007/s10115-024-02329-7Online publication date: 21-Feb-2025
https://doi.org/10.1007/s10115-024-02329-7
Gao JChen JOloulade BAl-Sabri RLyu TZhang JLi Z(2024)CommGNAS: Unsupervised Graph Neural Architecture Search for Community DetectionIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.327018112:2(444-454)Online publication date: Apr-2024
https://doi.org/10.1109/TETC.2023.3270181
Gao JWu ZAl-Sabri ROloulade BChen J(2024)AutoDDI: Drug–Drug Interaction Prediction With Automated Graph Neural NetworkIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.334957028:3(1773-1784)Online publication date: Mar-2024
https://doi.org/10.1109/JBHI.2024.3349570
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Index Terms

GraphPAS: Parallel Architecture Search for Graph Neural Networks
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

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Information

Published In

cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

Copyright © 2021 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2021

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Short-paper

Funding Sources

National Natural Science Foundation of China
Hunan Key Laboratory for Internet of Things in Electricity
Zhejiang Lab
CAAI-Huawei MindSpore Open Fund

Conference

SIGIR '21

Sponsor:

SIGIR

SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

Virtual Event, Canada

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

He CChen JLi QWang YGao J(2025)Decoupled graph neural architecture search with explainable variable propagation operationKnowledge and Information Systems10.1007/s10115-024-02329-7Online publication date: 21-Feb-2025
https://doi.org/10.1007/s10115-024-02329-7
Gao JChen JOloulade BAl-Sabri RLyu TZhang JLi Z(2024)CommGNAS: Unsupervised Graph Neural Architecture Search for Community DetectionIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.327018112:2(444-454)Online publication date: Apr-2024
https://doi.org/10.1109/TETC.2023.3270181
Gao JWu ZAl-Sabri ROloulade BChen J(2024)AutoDDI: Drug–Drug Interaction Prediction With Automated Graph Neural NetworkIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.334957028:3(1773-1784)Online publication date: Mar-2024
https://doi.org/10.1109/JBHI.2024.3349570
Wu ZChen JAl-Sabri ROloulade BGao J(2024)Depth-adaptive graph neural architecture search for graph classificationKnowledge-Based Systems10.1016/j.knosys.2024.112321301(112321)Online publication date: Oct-2024
https://doi.org/10.1016/j.knosys.2024.112321
Wu ZChen JAl-Sabri ROloulade BGao J(2023)Adaptive graph contrastive learning with joint optimization of data augmentation and graph encoderKnowledge and Information Systems10.1007/s10115-023-01979-366:3(1657-1681)Online publication date: 12-Oct-2023
https://dl.acm.org/doi/10.1007/s10115-023-01979-3
Gao JAl-Sabri ROloulade BChen JLyu TWu Z(2023)GM2NAS: multitask multiview graph neural architecture searchKnowledge and Information Systems10.1007/s10115-023-01886-765:10(4021-4054)Online publication date: 15-May-2023
https://dl.acm.org/doi/10.1007/s10115-023-01886-7
Yuan PSun YWang H(2022)Heterogeneous Information Network-Based Recommendation with Metapath Search and Memory Network Architecture SearchMathematics10.3390/math1016289510:16(2895)Online publication date: 12-Aug-2022
https://doi.org/10.3390/math10162895
Wei LZhao HHe Z(2022)Designing the Topology of Graph Neural Networks: A Novel Feature Fusion PerspectiveProceedings of the ACM Web Conference 202210.1145/3485447.3512185(1381-1391)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3512185
Al-Sabri RGao JChen JOloulade BLyu T(2022)Multi-View Graph Neural Architecture Search for Biomedical Entity and Relation ExtractionIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2022.320511320:2(1221-1233)Online publication date: 8-Sep-2022
https://dl.acm.org/doi/10.1109/TCBB.2022.3205113
Chen JGao JLyu TOloulade BHu X(2022)AutoMSR: Auto Molecular Structure Representation Learning for Multi-label Metabolic Pathway PredictionIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2022.319811920:6(3430-3439)Online publication date: 22-Aug-2022
https://dl.acm.org/doi/10.1109/TCBB.2022.3198119
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