reviews: 
Comments: The paper introduces GECo, a new approach designed to improve the explainability of Graph Neural Networks (GNNs) for graph classification tasks by using graph communities. This method works by examining how different communities within a graph contribute to the classification results, generating a mask that highlights the most significant structures in the graph. GECo was assessed on both synthetic and real-world datasets, consistently outperforming existing explainability techniques, such as PGMExplainer, PGExplainer, GNNExplainer, and SubgraphX across different metrics. The findings show that GECo excels at identifying relevant features, satisfying necessity and sufficiency criteria, and aligning with ground-truth explanations. In terms of contribution, this paper  (1) introduces GECo, a novel method for enhancing GNN explainability based on graph communities. (2) demonstrates its great performance and efficiency over previous methods on several datasets.
Strengths: 1.	The paper demonstrates that GECo outperforms existing methods in explainability across various datasets. It also highlights GECo's efficiency, offering faster computation times compared to other approaches.
Weaknesses: 1. Lack of novelty compared to the conventional graph community method. The proposed explainability method is based on detecting contributory substructures, specifically graph communities. However, the authors do not clearly distinguish the novelty of this approach from conventional graph community-based methods. Since substructure discovery is a widely adopted strategy for explaining GNNs, a more detailed demonstration—either theoretical or empirical—of how the proposed method advances existing graph community approaches is necessary for this work. For example, the paper No.1, Aviyente, Selin, and Abdullah Karaaslanli. "Explainability in Graph Data Science: Interpretability, replicability, and reproducibility of community detection." IEEE Signal Processing Magazine 39.4 (2022): 25-39., and paper No.2 Sangaiah, Arun Kumar, et al. "Explainable AI in big data intelligence of community detection for digitalization e-healthcare services." Applied Soft Computing 136 (2023): 110119. present community-based methods to explain GNNs. A comparison and analysis between your proposed methods and previous ones can be included in this part.

2. Lack of contribution. The abstract and introduction do not clearly articulate the motivation behind the proposed approach or highlight its novelty compared to existing methods. The contribution of using the graph community method for explainability should be well demonstrated in the paper, e.g., how is the proposed method different from the previous studies, and how does this study contribute to the graph community-based explainability methods?

3. Weak and unclear presentation and writing through the paper: 
(1) The introduction contains loosely related details and lacks a clear explanation of the background, which may distract readers from the main focus of the work. This work presents a very brief summary of the study in the third paragraph, which doesn’t give readers a clear sense of why using graph communities for explainability is effective. Therefore, a more detailed introduction of the proposed method is needed.
(2) The related work section should be more systematically organized, ideally comparing the strengths and weaknesses of various GNN explainability methods. For example, an organized explainability method for graph explainability is preferred, e.g., following a widely-adopted classification of GNN explainability approaches such as perturbation-based methods, gradient-based, decomposition-based methods, etc., in the related work part will be preferred.
(3) The tables and text on page 8 are not presented professionally or cohesively. More specifically, a more organized arrangement, including both text and table, is preferred.
(4) Section "3.2 The Proposed Methodology" focuses solely on technical details without providing any analysis that would strengthen the theoretical foundation of the proposed approach. For example, providing more details about how the proposed approach detects optimal subgraphs can be helpful for readers to understand the principle behind this method, e.g., mathematical formulation to explain the subgraph detection process.

4. Lack of clear visualization in Fig.1. The workflow in Figure 1 lacks clarity, particularly in how key components, such as the threshold value, are determined. The figure does not clearly illustrate the process, leaving important aspects of the methodology underexplained. A more detailed and explicit visual representation is needed to improve understanding. For example, how to perform graph community in step 2 and how to identify the most influential community can be visualized clearly

5. Lack of sufficient reference. The references cited in this work are insufficient to comprehensively support the proposed methodology. Furthermore, a significant portion of the referenced works are not up-to-date, failing to incorporate the most recent advancements in the field. To strengthen the credibility and relevance of the study, the authors need to include more current and pertinent literature that reflects the latest developments in the area of research. For example, these papers are related to your study: the paper uses graph community for explainability, Aviyente, Selin, and Abdullah Karaaslanli. "Explainability in Graph Data Science: Interpretability, replicability, and reproducibility of community detection." IEEE Signal Processing Magazine 39.4 (2022): 25-39, Sadler, Sophie, Derek Greene, and Daniel Archambault. "Towards explainable community finding." Applied Network Science 7.1 (2022): 81., Martínez Mora, Andrés, et al. "Community-aware explanations in knowledge graphs with XP-GNN." bioRxiv (2024): 2024-01.
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 2
Clarity: 1