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Update dataset card with hold-out experiment results

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@@ -190,6 +190,43 @@ All columns from `nodes.csv` plus:
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  - **DAG layers**: Kahn's algorithm; cycle nodes get layer=-1; namespace graph condensed via SCC
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  - **Namespace cycles**: 6,055 of 10,097 namespaces in 38 SCCs (largest: 5,899 nodes)
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  ## License
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  Apache 2.0
 
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  - **DAG layers**: Kahn's algorithm; cycle nodes get layer=-1; namespace graph condensed via SCC
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  - **Namespace cycles**: 6,055 of 10,097 namespaces in 38 SCCs (largest: 5,899 nodes)
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+ ## Hold-Out Experiments
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+ We validate the premise retrieval results with two levels of hold-out experiments to assess information leakage. In the original experiment, all network features (degree, PageRank, betweenness, community, DAG layer) are precomputed on the full graph. Hold-out experiments recompute features on a reduced graph to test whether full-graph computation inflates AUC.
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+ ### Edge-level hold-out
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+ - Randomly remove 20% of edges, recompute all features on remaining 80% graph
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+ - Tests whether full-graph feature computation inflates AUC
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+
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+ ### Declaration-level hold-out
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+ - Split theorems 80/20, remove ALL edges (incoming and outgoing) of test theorems from training graph
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+ - Test theorems have zero degree in training graph
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+ - Simulates the real scenario: predicting premises for a brand new theorem
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+ ### Results (Split 1, seed=42)
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+ | Method | AUC (original) | AUC (edge hold-out) | AUC (decl hold-out) |
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+ |--------|---------------|---------------------|---------------------|
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+ | Random | 0.499 | 0.497 | 0.500 |
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+ | Same module | 0.563 | 0.562 | 0.563 |
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+ | Same namespace | 0.590 | 0.590 | 0.592 |
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+ | Same community | 0.768 | 0.755 | 0.500 |
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+ | Network features | 0.991 | 0.988 | 0.978 |
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+ | All features | 0.994 | 0.992 | 0.984 |
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+ The community feature drops to random (0.500) in declaration-level hold-out because test declarations become isolated nodes with unique community IDs. Despite this, the combined network feature model retains AUC=0.978, confirming that degree, PageRank, and DAG position carry genuine predictive signal independent of information leakage.
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+ Full 5-split results with mean and std are in `experiments/holdout_decl_level.json` (updated incrementally).
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+
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+ ### Experiment Files
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+ | File | Description |
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+ |------|-------------|
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+ | `experiments/premise_retrieval_results.json` | Original experiment: 6 methods, 4 metrics, 95% CI |
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+ | `experiments/premise_retrieval_hard_negatives.json` | Hard negatives (same-community) variant |
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+ | `experiments/holdout_edge_level.json` | Edge-level hold-out (1 split) |
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+ | `experiments/holdout_decl_level.json` | Declaration-level hold-out (5 splits, incremental) |
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+
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  ## License
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  Apache 2.0