Эксперимент: GECC-Gated Loop-Aware Message Passing (#822)

{ "worked": true, "confidence": 8, "verdict": "Built an exact n=1 ego-intersection implementation with closure/r features, learned GECC gating, a baseline GCN, and a reproducible toy experiment. The mechanism checks passed: closure slope matched the predicted 1/(k+1) within 6.2e-14, the alpha=0.5 transition was observed at C=0.29 versus predicted 0.286, monotonic gating held, and update interpolation slope matched exactly. In the toy node-classification run, GECC also improved test error from 0.167 to 0.042, though this is only a small synthetic result.", "metrics": { "baseline": "Toy graph: all-node error 0.0833, test error 0.1667", "idea": "Toy graph: all-node error 0.0208, test error 0.0417; mean directed-edge C=0.4453; closure slope observed 0.142857 vs predicted 0.142857; gate transition observed C=0.29 vs predicted 0.286; update slope observed -1.500000 vs predicted -1.500000" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 gecc_demo.py", "files": [ "gecc_demo.py", "results.json" ], "limitations": "Only n=1 intersections and one small synthetic graph were tested; no Cora/Citeseer, multi-layer oversmoothing study, low/high-closure controlled sweep, ablation, FLOP/speed comparison, or repeated random seeds was run. The implementation uses Python-level edge loops and is an MVP rather than an optimized training layer." }