reviews: 
Comments: The paper explores the challenge of evaluating well-trained Graph Neural Network (GNN) models in real-world scenarios where test graph data lacks labels and may differ from the training data's distribution. To address this, the authors propose "Learning Behavior Discrepancy" (LEBED) as a score to assess a GNN's generalization ability under test-time distribution shifts. LEBED measures differences in learning behavior between a GNN trained on the training graph and one retrained on the test graph, considering both node prediction and structure reconstruction discrepancies. A novel GNN re-training strategy is introduced for this purpose. The paper's experiments demonstrate that the LEBED score strongly correlates with ground-truth test errors for various GNN models, offering an effective evaluation metric. The paper acknowledges certain assumptions and suggests addressing more complex distribution shifts in future research.
Strengths: 1.	Novel Research Problem: The paper addresses a novel research problem, online GNN evaluation. Previous works only consider test the GNN performance after training it. This paper proposes a new research problem that how to evaluate GNN’s performance under distribution shifts and no access to the original training data. From my perspective, it is an important and practical concern in the field of graph neural networks. This is a significant contribution as it explores the evaluation of GNNs under real-world conditions with distribution shifts and the absence of test node labels. It also paves the way to pre-training large-scale GNN models.

2.	Proposed Metric (LEBED): The paper introduces a novel metric, LEBED (Learning Behavior Discrepancy), which aims to estimate the generalization error of well-trained GNN models under distribution shifts. It re-trains the GNN under test data and then calculate the discrepancy of optimal weights between training and test data. Fig 2 gives a clear framework.

3.	Experimental Validation: The paper conducts extensive experiments on real-world test graphs under diverse distribution shifts, demonstrating the effectiveness of the proposed method. Datasets of various distribution shifts scenarios are provided, including node feature shifts, domain shifts and temporal shifts.
Weaknesses: 1.	Table 1 shows the test-time graph data. I wonder where did you get the pre-trained GNNs? What datasets were used for pre-training?
2.	In S2, GNN is re-trained was test data. However, if the same training data is used during S2, it will also cause difference of the optimal weights due to the randomness. Will this affect the LeBed score? In other words, is part of LeBed score caused by the randomness of training process? 
3.	Eq. 4 shows that Psuedo label $\mathbf{Y}_{\mathrm{te}}^*$ is from pre-trained GNN with test-time data, which has distribution shifts from original training data. Why is it can be used for the supervision of re-training? More explanations are expected here.
Recommendation: 8: accept, good paper
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 3 good