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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2411.00653 |
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| _version_ | 1866910680712478720 |
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| author | Lin, Ying-Chun Neville, Jennifer Becker, Cassiano Metha, Purvanshi Asghar, Nabiha Agarwal, Vipul |
| author_facet | Lin, Ying-Chun Neville, Jennifer Becker, Cassiano Metha, Purvanshi Asghar, Nabiha Agarwal, Vipul |
| contents | Understanding node representations in graph-based models is crucial for uncovering biases ,diagnosing errors, and building trust in model decisions. However, previous work on explainable AI for node representations has primarily emphasized explanations (reasons for model predictions) rather than interpretations (mapping representations to understandable concepts). Furthermore, the limited research that focuses on interpretation lacks validation, and thus the reliability of such methods is unclear. We address this gap by proposing a novel interpretation method-Node Coherence Rate for Representation Interpretation (NCI)-which quantifies how well different node relations are captured in node representations. We also propose a novel method (IME) to evaluate the accuracy of different interpretation methods. Our experimental results demonstrate that NCI reduces the error of the previous best approach by an average of 39%. We then apply NCI to derive insights about the node representations produced by several graph-based methods and assess their quality in unsupervised settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00653 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rethinking Node Representation Interpretation through Relation Coherence Lin, Ying-Chun Neville, Jennifer Becker, Cassiano Metha, Purvanshi Asghar, Nabiha Agarwal, Vipul Machine Learning Understanding node representations in graph-based models is crucial for uncovering biases ,diagnosing errors, and building trust in model decisions. However, previous work on explainable AI for node representations has primarily emphasized explanations (reasons for model predictions) rather than interpretations (mapping representations to understandable concepts). Furthermore, the limited research that focuses on interpretation lacks validation, and thus the reliability of such methods is unclear. We address this gap by proposing a novel interpretation method-Node Coherence Rate for Representation Interpretation (NCI)-which quantifies how well different node relations are captured in node representations. We also propose a novel method (IME) to evaluate the accuracy of different interpretation methods. Our experimental results demonstrate that NCI reduces the error of the previous best approach by an average of 39%. We then apply NCI to derive insights about the node representations produced by several graph-based methods and assess their quality in unsupervised settings. |
| title | Rethinking Node Representation Interpretation through Relation Coherence |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.00653 |