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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2509.23923 |
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| _version_ | 1866917048809947136 |
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| author | Bechler-Speicher, Maya Zerio, Andrea Huri, Maor Vestergaard, Marie Vibeke Gilad-Bachrach, Ran Jess, Tine Bhatt, Samir Sazonovs, Aleksejs |
| author_facet | Bechler-Speicher, Maya Zerio, Andrea Huri, Maor Vestergaard, Marie Vibeke Gilad-Bachrach, Ran Jess, Tine Bhatt, Samir Sazonovs, Aleksejs |
| contents | We introduce GMAN, a flexible, interpretable, and expressive framework that extends Graph Neural Additive Networks (GNANs) to learn from sets of sparse time-series data. GMAN represents each time-dependent trajectory as a directed graph and applies an enriched, more expressive GNAN to each graph. It allows users to control the interpretability-expressivity trade-off by grouping features and graphs to encode priors, and it provides feature, node, and graph-level interpretability. On real-world datasets, including mortality prediction from blood tests and fake-news detection, GMAN outperforms strong non-interpretable black-box baselines while delivering actionable, domain-aligned explanations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23923 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Mixing Additive Networks Bechler-Speicher, Maya Zerio, Andrea Huri, Maor Vestergaard, Marie Vibeke Gilad-Bachrach, Ran Jess, Tine Bhatt, Samir Sazonovs, Aleksejs Machine Learning Artificial Intelligence We introduce GMAN, a flexible, interpretable, and expressive framework that extends Graph Neural Additive Networks (GNANs) to learn from sets of sparse time-series data. GMAN represents each time-dependent trajectory as a directed graph and applies an enriched, more expressive GNAN to each graph. It allows users to control the interpretability-expressivity trade-off by grouping features and graphs to encode priors, and it provides feature, node, and graph-level interpretability. On real-world datasets, including mortality prediction from blood tests and fake-news detection, GMAN outperforms strong non-interpretable black-box baselines while delivering actionable, domain-aligned explanations. |
| title | Graph Mixing Additive Networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.23923 |