research-article

PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm

Authors:

Bin CuiAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

Pages 1817 - 1828

https://doi.org/10.1145/3485447.3511986

Published: 25 April 2022 Publication History

Abstract

Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-based tasks. However, as mainstream GNNs are designed based on the neural message passing mechanism, they do not scale well to data size and message passing steps. Although there has been an emerging interest in the design of scalable GNNs, current researches focus on specific GNN design, rather than the general design space, limiting the discovery of potential scalable GNN models. This paper proposes PaSca, a new paradigm and system that offers a principled approach to systemically construct and explore the design space for scalable GNNs, rather than studying individual designs. Through deconstructing the message passing mechanism, PaSca presents a novel Scalable Graph Neural Architecture Paradigm (SGAP), together with a general architecture design space consisting of 150k different designs. Following the paradigm, we implement an auto-search engine that can automatically search well-performing and scalable GNN architectures to balance the trade-off between multiple criteria (e.g., accuracy and efficiency) via multi-objective optimization. Empirical studies on ten benchmark datasets demonstrate that the representative instances (i.e., PaSca-V1, V2, and V3) discovered by our system achieve consistent performance among competitive baselines. Concretely, PaSca-V3 outperforms the state-of-the-art GNN method JK-Net by 0.4% in terms of predictive accuracy on our large industry dataset while achieving up to 28.3 × training speedups.

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Cited By

Li XLiao MWu ZSu DZhang WLi RWang G(2024)LightDiC: A Simple Yet Effective Approach for Large-Scale Digraph Representation LearningProceedings of the VLDB Endowment10.14778/3654621.365462317:7(1542-1551)Online publication date: 30-May-2024
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Yuan HLiu YZhang YAi XWang QChen CGu YYu G(2024)Comprehensive Evaluation of GNN Training Systems: A Data Management PerspectiveProceedings of the VLDB Endowment10.14778/3648160.364816717:6(1241-1254)Online publication date: 3-May-2024
https://dl.acm.org/doi/10.14778/3648160.3648167
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https://dl.acm.org/doi/10.1145/3626246.3653399
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Index Terms

PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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WWW '22: The ACM Web Conference 2022

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https://dl.acm.org/doi/10.14778/3654621.3654623
Yuan HLiu YZhang YAi XWang QChen CGu YYu G(2024)Comprehensive Evaluation of GNN Training Systems: A Data Management PerspectiveProceedings of the VLDB Endowment10.14778/3648160.364816717:6(1241-1254)Online publication date: 3-May-2024
https://dl.acm.org/doi/10.14778/3648160.3648167
Zhang WYan GShen YLing YTao YCui BTang JBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)NPA: Improving Large-scale Graph Neural Networks with Non-parametric AttentionCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3653399(414-427)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3653399
Li XMa JWu ZSu DZhang WLi RWang GChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Rethinking Node-wise Propagation for Large-scale Graph LearningProceedings of the ACM on Web Conference 202410.1145/3589334.3645450(560-569)Online publication date: 13-May-2024
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