Graph Additive Models disentangling feature, topology, and interactions

Graph Neural Additive Networks (GNANs) have emerged as an effective, interpretable-by-design graph architecture for learning on graphs. They achieve model transparency by combining per-feature shape functions with a simple distance-based reweighting. Despite the effectiveness of GNAN, we identify the following limitations: i) node-wise relevance scores are computed as a weighted aggregation over all graph nodes, allowing irrelevant nodes to inherit spurious relevance from relevant ones; ii) graph topology is encoded only through shortest-path distances, and structurally distinct graphs may have identical distances; iii) feature and distance information are combined multiplicatively, thus entangling feature, distance, and interaction effects into a single, rescalable term. To address these limitations, we introduce AMIGA, which represents topology through a vocabulary of mined motifs and decomposes each node’s contribution into three additive channels: a feature channel, a structural channel, and a feature–structure interaction channel. We center these channels around a training-data baseline that gives every graph equal weight, regardless of its size, making the decomposition well defined and unique. Across ten graph classification and regression benchmarks, AMIGA outperforms GNAN on most datasets and narrows the gap to standard black-box GNNs. Because every channel is centered and attached to a single node, AMIGA’s explanations stay local to the node that generates them and reconstruct the model’s output exactly while providing interpretability at different levels of granularity. Code is publicly available online.