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Graph Mixing Additive Networks

arXiv 2509.23923 ↗First posted 28 Sep 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 48 references that are in the map. First posted on arXiv 28 Sep 2025.

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References · 16

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  4. 2021How Attentive are Graph Attention Networks?cited by 818
  5. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 577
  6. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  7. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 548
  8. 2018BRITS: Bidirectional Recurrent Imputation for Time Seriescited by 288
  9. 2021Multi-Time Attention Networks for Irregularly Sampled Time Seriescited by 234
  10. 2022SAITS: Self-Attention-based Imputation for Time Seriescited by 167
  11. 2019Set Functions for Time Seriescited by 137
  12. 2019Fake News Detection on Social Media using Geometric Deep Learningcited by 115
4 more
  1. 2021Graph-Guided Network for Irregularly Sampled Multivariate Time Seriescited by 110
  2. 2019Interpolation-Prediction Networks for Irregularly Sampled Time Seriescited by 89
  3. 2021Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Seriescited by 30
  4. 2024The Interpretable and Effective Graph Neural Additive Networkscited by 19

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