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Node Information Awareness Pooling for Graph Representation Learning

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Advances in Knowledge Discovery and Data Mining (PAKDD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13280))

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Abstract

Graph neural networks (GNNs), which generalize deep neural network models to graph structure data, have attracted increasing attention and achieved state-of-the-art performance in graph-related tasks such as graph classification, link prediction, and node classification. To adapt GNNs to graph classification, existing works aim to define the graph pooling method to learn graph-level representation by downsampling and summarizing the information present in the nodes. However, most existing pooling methods lack a way of obtaining information about the entire graph from both the local and global aspects of the graph. Moreover, in these pooling methods, the difference features between nodes and their neighbors are usually ignored, which is crucial in obtaining graph information in our opinions. In this paper, we propose a novel graph pooling method called Node Information Awareness Pooling (NIAPool), which addresses the limitations of previous graph pooling methods. NIAPool utilizes a novel self-attention framework and a new convolution operation that can better capture the difference features between nodes to obtain node information in the graph from both local and global aspects. Experiments on five public benchmark datasets demonstrate the superior performance of NIAPool for graph classification compared to the state-of-the-art baseline methods.

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Acknowledgments

This work was supported by the National Key R&D Program of China (2017YFB0202403) and the Key R&D Program of Sichuan Province, China (2017GZDZX0003, 2020YFG0089, 2020YFG0308, 2020YFG0304).

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, China

    Chuan Sun, Feihu Huang & Jian Peng

Authors
  1. Chuan Sun
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  2. Feihu Huang
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  3. Jian Peng
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Corresponding author

Correspondence to Jian Peng .

Editor information

Editors and Affiliations

  1. Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal

    João Gama

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Tianrui Li

  3. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Yang Yu

  4. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Enhong Chen

  5. JD iCity, JD Technology & JD Intelligent Cities Research, Beijing, China

    Yu Zheng

  6. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Fei Teng

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Sun, C., Huang, F., Peng, J. (2022). Node Information Awareness Pooling for Graph Representation Learning. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13280. Springer, Cham. https://doi.org/10.1007/978-3-031-05933-9_15

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  • DOI: https://doi.org/10.1007/978-3-031-05933-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05932-2

  • Online ISBN: 978-3-031-05933-9

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