Significant performance gap on cross-domain evaluation

[wengguanglong]

🐛 Reproduction Issue: Significant performance gap on cross-domain evaluation

Summary

I tried to reproduce SubgraphRAG following the official code and default settings, but observed a significant performance gap, especially on cross-domain evaluation (WebQSP ↔ CWQ). The in-domain results are relatively close, but cross-domain performance is far below the reported numbers.

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Results Comparison (K=100)

Scenario	Metric	My Result	Paper Result	Gap
WebQSP → WebQSP	Answer Recall	0.871	0.944	-0.073
	Shortest Path Triple Recall	0.794	0.883	-0.089
	GPT-4o Triple Recall	0.732	0.865	-0.133
CWQ → CWQ	Answer Recall	0.895	0.914	-0.019
	Shortest Path Triple Recall	0.759	0.811	-0.052
	GPT-4o Triple Recall	0.739	0.840	-0.101
WebQSP → CWQ	Answer Recall	0.560	0.887	-0.327
	Shortest Path Triple Recall	0.374	0.794	-0.420
	GPT-4o Triple Recall	0.363	0.776	-0.413
CWQ → WebQSP	Answer Recall	0.203	0.773	-0.570
	Shortest Path Triple Recall	0.114	0.622	-0.508
	GPT-4o Triple Recall	0.124	0.623	-0.499

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Training Results (Validation Set)

Dataset	loss	val/ans_recall@100	val/triple_recall@100	Best Epoch	Total Epochs	Patience
WebQSP	0.01183	0.89683	0.82729	111	126	15
CWQ	0.00567	0.91499	0.77503	130	145	15

Observation: Training converged properly with reasonable validation metrics. The model seems to learn well on the training/validation sets.

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Experimental Setup

Hyperparameters

• seed = 42 (default in source code)
• lr = 1e-4
• patience = 15
• K = 100 (for evaluation)

Run Commands

# Training
python train.py -d webqsp
python train.py -d cwq

# Evaluation (in-domain)
python eval.py -d webqsp -p /path/to/webqsp_retrieval_result.pth
python eval.py -d cwq -p /path/to/cwq_retrieval_result.pth

# Evaluation (cross-domain)
python eval.py -d cwq -p /path/to/webqsp_retrieval_result.pth
python eval.py -d webqsp -p /path/to/cwq_retrieval_result.pth

**Training (val set):**
- WebQSP: loss=0.01183, ans_recall=0.8968, triple_recall=0.8273 @ epoch 111
- CWQ: loss=0.00567, ans_recall=0.9150, triple_recall=0.7750 @ epoch 130

**Setup:** seed=42, lr=1e-4, patience=15, PyTorch 2.5.1+cu121, CUDA 12.4, GPU NVIDIA A40

**Tried:** default seed, patience tuning, multiple seeds (32,52) — gap persists

Any suggestions? Thanks!