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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.07648 |
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| _version_ | 1866909778914050048 |
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| author | Simpson, Lachlan Millar, Kyle Cheng, Adriel Lim, Cheng-Chew Chew, Hong Gunn |
| author_facet | Simpson, Lachlan Millar, Kyle Cheng, Adriel Lim, Cheng-Chew Chew, Hong Gunn |
| contents | Integrated Gradients (IG) is a common explainability technique to address the black-box problem of neural networks. Integrated gradients assumes continuous data. Graphs are discrete structures making IG ill-suited to graphs. In this work, we introduce graph-based integrated gradients (GB-IG); an extension of IG to graphs. We demonstrate on four synthetic datasets that GB-IG accurately identifies crucial structural components of the graph used in classification tasks. We further demonstrate on three prevalent real-world graph datasets that GB-IG outperforms IG in highlighting important features for node classification tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07648 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph-based Integrated Gradients for Explaining Graph Neural Networks Simpson, Lachlan Millar, Kyle Cheng, Adriel Lim, Cheng-Chew Chew, Hong Gunn Machine Learning Integrated Gradients (IG) is a common explainability technique to address the black-box problem of neural networks. Integrated gradients assumes continuous data. Graphs are discrete structures making IG ill-suited to graphs. In this work, we introduce graph-based integrated gradients (GB-IG); an extension of IG to graphs. We demonstrate on four synthetic datasets that GB-IG accurately identifies crucial structural components of the graph used in classification tasks. We further demonstrate on three prevalent real-world graph datasets that GB-IG outperforms IG in highlighting important features for node classification tasks. |
| title | Graph-based Integrated Gradients for Explaining Graph Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.07648 |