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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2412.11964 |
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| _version_ | 1866913614329282560 |
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| author | Sloneker, Whitney Patel, Shalin Wang, Michael Crawford, Lorin Singh, Ritambhara |
| author_facet | Sloneker, Whitney Patel, Shalin Wang, Michael Crawford, Lorin Singh, Ritambhara |
| contents | Graph neural networks (GNNs) are powerful tools for conducting inference on graph data but are often seen as "black boxes" due to difficulty in extracting meaningful subnetworks driving predictive performance. Many interpretable GNN methods exist, but they cannot quantify uncertainty in edge weights and suffer in predictive accuracy when applied to challenging graph structures. In this work, we proposed BetaExplainer which addresses these issues by using a sparsity-inducing prior to mask unimportant edges during model training. To evaluate our approach, we examine various simulated data sets with diverse real-world characteristics. Not only does this implementation provide a notion of edge importance uncertainty, it also improves upon evaluation metrics for challenging datasets compared to state-of-the art explainer methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11964 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks Sloneker, Whitney Patel, Shalin Wang, Michael Crawford, Lorin Singh, Ritambhara Machine Learning Graph neural networks (GNNs) are powerful tools for conducting inference on graph data but are often seen as "black boxes" due to difficulty in extracting meaningful subnetworks driving predictive performance. Many interpretable GNN methods exist, but they cannot quantify uncertainty in edge weights and suffer in predictive accuracy when applied to challenging graph structures. In this work, we proposed BetaExplainer which addresses these issues by using a sparsity-inducing prior to mask unimportant edges during model training. To evaluate our approach, we examine various simulated data sets with diverse real-world characteristics. Not only does this implementation provide a notion of edge importance uncertainty, it also improves upon evaluation metrics for challenging datasets compared to state-of-the art explainer methods. |
| title | BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.11964 |