research-article

Multi-scale Graph Pooling Approach with Adaptive Key Subgraph for Graph Representations

Authors:

Yiping SongAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 1736 - 1745

https://doi.org/10.1145/3583780.3614981

Published: 21 October 2023 Publication History

Abstract

The recent progress in graph representation learning boosts the development of many graph classification tasks, such as protein classification and social network classification. One of the mainstream approaches for graph representation learning is the hierarchical pooling method. It learns the graph representation by gradually reducing the scale of the graph, so it can be easily adapted to large-scale graphs. However, existing graph pooling methods discard the original graph structure during downsizing the graph, resulting in a lack of graph topological structure. In this paper, we propose a multi-scale graph neural network (MSGNN) model that not only retains the topological information of the graph but also maintains the key-subgraph for better interpretability. MSGNN gradually discards the unimportant nodes and retains the important subgraph structure during the iteration. The key subgraphs are first chosen by experience and then adaptively evolved to tailor specific graph structures for downstream tasks. The extensive experiments on seven datasets show that MSGNN improves the SOTA performance on graph classification and better retains key subgraphs.

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Cited By

Liu CYu WGao KMa XZhan YWu JHu WDu B(2025)Graph explicit pooling for graph-level representation learningNeural Networks10.1016/j.neunet.2024.106790181(106790)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106790
Nie MChen DWang DChen H(2025)AutoGSP: Automated graph-level representation learning via subgraph detection and propagation decelerationExpert Systems with Applications10.1016/j.eswa.2025.126871(126871)Online publication date: Feb-2025
https://doi.org/10.1016/j.eswa.2025.126871

Index Terms

Multi-scale Graph Pooling Approach with Adaptive Key Subgraph for Graph Representations
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
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Frank Hopfgartner
University of Koblenz, Germany
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Mark Lee
University of Birmingham, UK
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University of Birmingham, UK
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Program Chairs:
Mounia Lalmas
Spotify, UK
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Min Zhang
Tsinghua University, China
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Rodrygo Santos
Federal University of Minas Gerais, Brazil

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Published: 21 October 2023

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National Natural Science Foundation of China
Natural Science Foundation of Hunan Province

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October 21 - 25, 2023

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Cited By

Liu CYu WGao KMa XZhan YWu JHu WDu B(2025)Graph explicit pooling for graph-level representation learningNeural Networks10.1016/j.neunet.2024.106790181(106790)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106790
Nie MChen DWang DChen H(2025)AutoGSP: Automated graph-level representation learning via subgraph detection and propagation decelerationExpert Systems with Applications10.1016/j.eswa.2025.126871(126871)Online publication date: Feb-2025
https://doi.org/10.1016/j.eswa.2025.126871

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