HARMONIA: Interpretable Graph Learning through Mixtures of Neural Bases
Existing interpretable graph additive models still face limitations in either computational scalability or modeling flexibility. In terms of structural modeling, previous approaches either face quadratic scaling costs or sacrifice explicit source-to-target contribution decomposition. In terms of feature components, they rely either on per-feature neural networks or on single shared bases with limited feature specialization. We address both problems by introducing HARMONIA: Interpretable Graph Learning through Mixtures of Neural Bases, an interpretable-by-design framework. For feature modeling, HARMONIA introduces a Mixture of Neural Bases (MoNB), which routes features to specialized basis...
| Authors | Quan D. Bui, Nguyen Do, An Nguyen Dang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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