research-article

ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks

Authors:

Niusha Shafiabady,

Fang ChenAuthors Info & Claims

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

Pages 1259 - 1267

https://doi.org/10.1145/3583780.3614772

Published: 21 October 2023 Publication History

Abstract

Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery. To address this challenge and enable reliable decision-making, many GNN explainers have been proposed in recent years. However, these methods often encounter limitations, including their dependence on specific instances, lack of generalizability to unseen graphs, producing potentially invalid explanations, and yielding inadequate fidelity. To overcome these limitations, we, in this paper, introduce the Auxiliary Classifier Generative Adversarial Network (ACGAN) into the field of GNN explanation and propose a new GNN explainer dubbed ACGAN-GNNExplainer. Our approach leverages a generator to produce explanations for the original input graphs while incorporating a discriminator to oversee the generation process, ensuring explanation fidelity and improving accuracy. Experimental evaluations conducted on both synthetic and real-world graph datasets demonstrate the superiority of our proposed method compared to other existing GNN explainers.

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Cited By

Lu HWang LMa XCheng JZhou M(2025)A survey of graph neural networks and their industrial applicationsNeurocomputing10.1016/j.neucom.2024.128761614(128761)Online publication date: Jan-2025
https://doi.org/10.1016/j.neucom.2024.128761
Li YZhou JZheng BShafiabady NChen F(2024)Reliable and Faithful Generative Explainers for Graph Neural NetworksMachine Learning and Knowledge Extraction10.3390/make60401396:4(2913-2929)Online publication date: 18-Dec-2024
https://doi.org/10.3390/make6040139

Index Terms

ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Generative and developmental approaches
      2. Neural networks

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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

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Published: 21 October 2023

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October 21 - 25, 2023

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Cited By

Lu HWang LMa XCheng JZhou M(2025)A survey of graph neural networks and their industrial applicationsNeurocomputing10.1016/j.neucom.2024.128761614(128761)Online publication date: Jan-2025
https://doi.org/10.1016/j.neucom.2024.128761
Li YZhou JZheng BShafiabady NChen F(2024)Reliable and Faithful Generative Explainers for Graph Neural NetworksMachine Learning and Knowledge Extraction10.3390/make60401396:4(2913-2929)Online publication date: 18-Dec-2024
https://doi.org/10.3390/make6040139

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