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A Causal Disentangled Multi-granularity Graph Classification Method

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Rough Sets (IJCRS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14481))

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Abstract

Graph data widely exists in real life, with large amounts of data and complex structures. It is necessary to map graph data to low-dimensional embedding. Graph classification, a critical graph task, mainly relies on identifying the important substructures within the graph. At present, some graph classification methods do not combine the multi-granularity characteristics of graph data. This lack of granularity distinction in modeling leads to a conflation of key information and false correlations within the model. So, achieving the desired goal of a credible and interpretable model becomes challenging. This paper proposes a causal disentangled multi-granularity graph representation learning method (CDM-GNN) to solve this challenge. The CDM-GNN model disentangles the important substructures and bias parts within the graph from a multi-granularity perspective. The disentanglement of the CDM-GNN model reveals important and bias parts, forming the foundation for its classification task, specifically, model interpretations. The CDM-GNN model exhibits strong classification performance and generates explanatory outcomes aligning with human cognitive patterns. In order to verify the effectiveness of the model, this paper compares the three real-world datasets MUTAG, PTC, and IMDM-M. Six state-of-the-art models, namely GCN, GAT, Top-k, ASAPool, SUGAR, and SAT are employed for comparison purposes. Additionally, a qualitative analysis of the interpretation results is conducted.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Nos. 62221005, 61936001, 61806031), Natural Science Foundation of Chongqing, China (Nos. cstc2019jcyj-cxttX0002, cstc2021ycjh-bgzxm0013), Project of Chongqing Municipal Education Commission, China (No. HZ2021008), and Doctoral Innovation Talent Program of Chongqing University of Posts and Telecommunications, China (Nos. BYJS202108, BYJS202209, BYJS202118).

Author information

Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing, China

    Yuan Li, Li Liu, Penggang Chen, Youmin Zhang & Guoyin Wang

  2. Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, 400065, The People’s Republic of China

    Yuan Li, Li Liu, Penggang Chen, Youmin Zhang & Guoyin Wang

Authors
  1. Yuan Li
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  2. Li Liu
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  3. Penggang Chen
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  4. Youmin Zhang
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  5. Guoyin Wang
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Corresponding author

Correspondence to Guoyin Wang .

Editor information

Editors and Affiliations

  1. IRCCS Istituto Ortopedico Galeazzi, Milano, Italy

    Andrea Campagner

  2. Ghent University, Ghent, Belgium

    Oliver Urs Lenz

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Shuyin **a

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. AGH University of Science and Technology, Kraków, Poland

    Jarosław Wąs

  6. University of Regina, Regina, SK, Canada

    **gTao Yao

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Li, Y., Liu, L., Chen, P., Zhang, Y., Wang, G. (2023). A Causal Disentangled Multi-granularity Graph Classification Method. In: Campagner, A., Urs Lenz, O., **a, S., Ślęzak, D., Wąs, J., Yao, J. (eds) Rough Sets. IJCRS 2023. Lecture Notes in Computer Science(), vol 14481. Springer, Cham. https://doi.org/10.1007/978-3-031-50959-9_25

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  • DOI: https://doi.org/10.1007/978-3-031-50959-9_25

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  • Online ISBN: 978-3-031-50959-9

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