MSS: Multi-Semantic Spaces as a Declared Evidence Layer for Graph Anomaly Detection

Benchmarks for graph anomaly detection (GAD) usually change the evidence construction and the consuming model together, so a reported margin does not show which of the two produced it, and an aggregate score hides the view a prediction rests on. Holding the read-out fixed locates the gain in the construction rather than in the model that consumes it: on the same tree head, complete evidence improves the ten-graph mean AUPRC over its baseline feed by 1.65 points under full supervision and 4.17 under the 20+80 budget, and on the largest graph adding regional references to that feed moves AUPRC from 41.12 to 72.64. We propose Multi-Semantic Spaces (MSS), a framework that treats raw and graph-derived feature blocks as materials declared by explicit mechanism hypotheses. A frozen topology backbone, soft regional occupancy, and regional references organize these materials into positions and deviations from a declared comparison unit. MSS-T fits boosted trees to a fixed evidence table, while MSS-N learns private coordinates and per-space experts, updates its references during training, and retains per-space states through stop-gradient fusion. Complete evidence also improves a second library-default head. On ten public benchmarks, using the released GADBench splits and a common ten-seed list, one configuration shared across all ten datasets and never retuned attains the highest mean AUPRC under both budgets. It exceeds the strongest baseline by 2.57 points under full supervision and 4.69 under the 20+80 budget. MSS-N exceeds MSS-T on two fully supervised graphs and exposes coordinate-level diagnostics, so the gains stay attributable and the states inspectable.