Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: The authors present GraphEx, a model-level explainer for Graph Neural Networks (GNNs) that learns a graph generative model to approximate the distribution of graphs classified into a target class. GraphEx stands out by not requiring a black box deep model or white box access to the GNN, and it can generate diverse explanation graphs in one shot.
strengths_and_weaknesses: Strengths:

1. GraphEx's unique approach eliminates the need for an additional black box deep model to explain the GNN.

2. The ability to generate diverse explanation graphs with varying node and edge features in a single shot enhances the explainer's efficiency.

3. GraphEx does not require white box access to the GNN model, making it more user-friendly and applicable to a wider range of end-users.
suggested_changes: None
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper proposes a model-level explainer for graph neural network. It learns a graph generative model (GraphEx) to approximate the distribution of graphs classified into a target class by the GNN model. Then, GraphEx can construct representative examples belonging to the target class or the decision boundary.
strengths_and_weaknesses: [Strength]:
- The idea of providing insight into the model decision-making process rather than providing explanations for specific input data is promising
- It does not require another black box deep model to explain the GNN and can generate a diverse set of explanation graphs with different node and edge features in one shot.

[Weakness]:
- The proposed method seems to be similar to the GNNInterpreter, but the differences between them have not been fully discussed.
- The experimental results given in the paper are insufficient, so the effectiveness of the methods proposed in the paper cannot be determined.

suggested_changes: - In fact, the proposed method may have some limitations on the applicable dataset, such as whether a graph is a Gilbert random graph and the type of features of nodes and edges (continuous or concrete). I hope to see more relevant discussions.
- In the experimental part, it only compared with XGNN, which has been proposed for nearly three years. However, there are many updated and effective model-level interpretation methods, such as GANExplainer, Gem and GNNInterpreter, which are also based on generating models. I recommend to supplement experiments to compare with them.
review_title: null

Meta-review: metareview: Good paper with all reviewers arguing for acceptance. Discussion on the difference between GraphEx and GNNInterpreter is suggested.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to present