reviews: 
Comments: The authors investigate the behaviors of normal and anomalous nodes in a diffusion process. The authors made two observations that may help design new algorithms for supervised/semi-supervised anomaly detection. The first observation leads to algorithms that are capable of capturing local information in egonet. The second observation leads to the generation of additional graphs based on the diffusion process that can be utilized as auxiliary data to enhance the anomaly detectors. The authors then demonstrate the benefit of the models compared with baselines on several public datasets.
Strengths: Strengths:
- The authors present an interesting analysis of the diffusion process on a graph.
- The proposed approach based on the two observations is also interesting. 
- The first observation leads to three non-GNN methods for anomaly detection in graphs.
- The second observation leads to a graph generation technique that can help improve the performance of anomaly detectors.
- The authors demonstrate the benefit of the models against several GNN baselines.
- The graph generation procedure can be used to enhance many other GNN models with a slight modification of the training objective.
Weaknesses: I have a few concerns and questions regarding the paper:
1) One of my biggest concerns about this paper is its clarity of presentation. The paper is hard to understand. One factor is that the authors did not describe enough about how the diffusion process works on graphs. Section 3.2 only explains the generic diffusion process on regular data (x). How it applies in the context of graphs, which contain structure and attributes, is not clearly explained. After explaining Section 3.2, the authors directly jump to the graph observations without explaining how the diffusion process on graphs works.
2) Since the authors did not clearly explain the diffusion process on graphs, many questions may arise about this, such as:
    - How does the structure of the graph (A) contribute to the next node feature representation (X_{t+1})?
    - Any effect of the neighboring node on a particular node representation in the next step?
    - The diffusion process itself tries to model the data distribution, i.e., for graph cases, it models the data distribution of G={A, X}. As the first approach utilizes the diffusion information, why do the authors claim that the approach does not need to learn P(A, X)?
    - And more questions that depend on the answer to the questions above.
3) The principle derived from the first observation is not surprising, i.e., "The denoising neural network should be capable of capturing the local information in egonets". In fact, I would argue that this principle is the bedrock of nearly all graph anomaly detection models.  
4) The baselines used in the experiments are relatively inadequate, particularly in the second group. I would suggest the authors to add more baselines in the comparisons, such as:
    - GHRN [1]
    - GDN [2]
    - H2-FDetector [3]
    - GAGA [4]
    - etc.


[1] Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, and Yongdong Zhang. Addressing heterophily in graph anomaly detection: A perspective of graph spectrum. In Proceedings of the ACM Web Conference 2023, pp. 1528–1538, 2023a. 

[2] Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, and Yongdong Zhang. Alleviating structural distribution shift in graph anomaly detection. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pp. 357–365, 2023b.

[3] Fengzhao Shi, Yanan Cao, Yanmin Shang, Yuchen Zhou, Chuan Zhou, and Jia Wu. H2-fdetector: A gnn-based fraud detector with homophilic and heterophilic connections. In Proceedings of the ACM Web Conference 2022, pp. 1486–1494, 2022. 

[4] Yuchen Wang, Jinghui Zhang, Zhengjie Huang, Weibin Li, Shikun Feng, Ziheng Ma, Yu Sun, Dianhai Yu, Fang Dong, Jiahui Jin, et al. Label information enhanced fraud detection against low homophily in graphs. In Proceedings of the ACM Web Conference 2023, pp. 406–416, 2023.
Recommendation: 5: marginally below the acceptance threshold
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: 1 poor