Node Signal Atlas: Mapping Predictive Signal Beyond Homophily

Node classification benchmarks often favor very different model interfaces, yet these differences are commonly summarized by homophily. We introduce the *Node Signal Atlas* (NSA), a lightweight pre-training diagnostic that characterizes a dataset through three marginal signal channels: the*Feature Signal Score* (FSS), the *Topology Signal Score* (TSS), and the *Label Signal Score* (LSS). Each coordinate is computed using the same fixed logistic-regression probe on inexpensive annulus-based descriptors, without training a GNN. On a controlled 24-dataset synthetic suite, NSA responds systematically to feature, structural-role, and local label-context manipulations, captures mixed-signal regimes, and exposes an interaction-only boundary outside its marginal scope. Across 20 real benchmarks, NSA separates datasets with nearly identical homophily but substantially different signal profiles and model behavior. Finally, on a frozen cross-generator architecture-selection benchmark, NSA improves over scalar node homophily, while a richer homophily profile is a stronger standalone baseline; combining the two yields the best low-budget triage. NSA thus complements homophily by describing *where predictive signal is accessible*.