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…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
