Graph Additive Models disentangling feature, topology, and interactions
Read the original on arXiv Machine Learning →Graph Additive Models (AMIGA) address three key limitations of Graph Neural Additive Networks (GNANs): they prevent irrelevant nodes from inheriting spurious relevance, represent topology via mined motifs instead of only shortest‑path distances, and separate feature, structure, and interaction effects into distinct additive channels. AMIGA’s decomposition is centered around a graph‑balanced training baseline, ensuring a unique and well‑defined representation for each node. Across ten graph classification and regression benchmarks, AMIGA outperforms GNAN on most datasets and narrows the performance gap with standard black‑box GNNs, while providing node‑local explanations that exactly reconstruct the model output.
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