Every rung runs on the same split, the same negatives, the same endpoints, and at least five seeds with bootstrap confidence intervals. One seed cannot resolve a 0.02 difference in average precision, and most reported wins on biomedical knowledge graphs sit inside that band.
0 Base rate —
What does guessing the prior achieve?
A prevalence constant, equivalent to sklearn DummyClassifier(strategy="prior")
If this rung is not beaten: Anchors average precision. Anything not beating it is noise.
1 Degree-only null Q1
Does simple popularity explain performance?
sklearn LogisticRegression on log1p(per-relation degree). No biology, no relations, no message passing.
If this rung is not beaten: The graph encodes study attention, not safety biology. The project returns a negative result, and that is a real finding.
2 Non-graph model Q2
Do target features alone explain performance?
sklearn HistGradientBoostingClassifier and a tabular MLP on target annotations only: GTEx expression breadth, gnomAD pLI and LOEUF, protein family, subcellular localisation. Zero graph structure.
If this rung is not beaten: The graph adds nothing. Ship the cheaper tabular model and close the project.
2b Graph-topology GBM Q2
Do hand-computed graph statistics suffice?
sklearn HistGradientBoostingClassifier on personalised PageRank to known-risky targets, betweenness, k-core, clustering, degree. Graph-derived, but no learned representation.
If this rung is not beaten: A published comparator of this family reached ROC-AUC around 0.71 on target-level safety attrition. That is the bar a GNN must clear.
3 Shallow KGE Q3
Does embedding entities suffice, without message passing?
PyKEEN ComplEx and PyKEEN RotatE: relation-typed scoring functions with no neighbourhood aggregation.
If this rung is not beaten: Relation-typed embedding is what helps; aggregation is not.
4 Relation-agnostic GNN Q3
Does connectivity alone help?
PyG GraphSAGE (SAGEConv) and PyG GAT (GATConv) over a collapsed single adjacency, with edge types deliberately discarded.
If this rung is not beaten: The control that isolates relation types. Without it, no claim about relationships can be made at all.
5 Relation-aware GNN Q3
Do relation TYPES add information?
PyG R-GCN (RGCNConv), CompGCN and PyG HGT (HGTConv), with a degree-offset head and a gradient-reversal adversary.
If this rung is not beaten: The difference between this rung and rung 4 IS the research answer. If it is near zero, connectivity matters and relation types do not.