Seeing More, Distinguishing Less? Learning from Graph Propagation States and Changes

Decoupled graph neural networks precompute multi-hop features through graph diffusion, enabling information from different propagation scales to be used without increasing neural network depth. However, existing cross-hop fusion methods often assign a single weight to each propagation step and share it across feature dimensions, ignoring that different features could benefit from different propagation scales. Moreover, using more propagation scales does not necessarily improve node distinguishability, as combining multi-hop states can weaken useful differences across propagation steps. To this end, we propose Feature-wise Fusion across Hops for Adaptive Graph Learning (FOCAL), a graph learning framework that models Propagation States and Changes through feature-wise cross-hop fusion. In the method, Propagation States describe feature values after diffusion, while Propagation Changes capture their differences between consecutive diffusion steps. FOCAL learns independent weights for each feature in the State and Change branches, allowing different features to use different combinations of propagation operators and diffusion steps. Node-level gates further adjust the contributions of the encoded State and Change representations for each node. Experiments on eight node classification datasets show that FOCAL achieves the highest accuracy on seven datasets. Ablation studies and weight analysis further demonstrate the contributions of Propagation Changes, feature-wise fusion, separate State and Change weights, and Node gates.