Kati8/ICLR_2023_2025 huggingface dataset:
authors: Xiaoxiao Ma
authors: Ruikun Li
authors: Fanzhen Liu
authors: Kaize Ding
authors: Jia Wu
reviews: 
Comments: This paper proposes denoising diffusion and graph generative models for detecting graph anomalies. The denoising diffusion model learns to reconstruct the original graph from noisy samples, while the graph generative model learns to generate new graphs that align with the original graph semantics. The authors evaluate the proposed methods on six datasets and compare them with several state-of-the-art methods, showing promising results in terms of detection accuracy, precision, recall, and AUC. Overall, this paper presents valuable contributions to the field of graph anomaly detection, with potential applications in various domains.
Strengths: 1. the authors of this paper present their proposed methods and results in a clear and organized manner, and they provide a thorough discussion of related work and future research directions.

2. Authors integrate graph diffusion into the GAD task to address the challenges of limited prior information about anomalous nodes by generating more training samples.
Weaknesses: 1. Graph anomaly detection often includes two types: attribute anomalies and structural anomalies. The author disrupts the original distribution by continuously injecting noise, which may be useful for detecting attribute anomalies. However, is this also effective for detecting structural anomalies?

2.  when injecting scheduled noise to node attributes as the forward diffusion process, anomalies’ egonet dissimilarities change more dramatically than normal nodes, Please provide a clear explanation for this phenomenon.   To calculate egonet dissimilarities, you utilize the formula: LX, which is a graph convolution operation without learnable parameters, why LX can be egonet dissimilarities?
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: 3 good
reviews: 
Comments: This paper proposes a DiffAD, which is a DDPM-based anomaly detector to detect anomalous nodes in a graph that does not need to learn the data distribution explicitly to detect anomalies. The proposed model first learns the diffusion dynamics of ego-net dissimilarities of diffusion steps and then generates auxiliary training samples to enhance a detector's performance. The empirical results on six real-world datasets illustrate the effectiveness of the proposed model.
Strengths: 1. The paper provides evidence on the reason why the forward and denoised processes are designed. 
2. The empirical results are very promising, DiffAD outperforms SOTA  by a large margin.
3. The loss function can resolve the class imbalance in anomaly datasets.
Weaknesses: 1. The definition of 'ego-net dissimilarity` is actually a GNN embedding in literature, so 'dissimilarity` is an inaccurate word.
2. The time complexity should be very high due to the nature of the transformer and DDPM.
3. The motivation is to learn to classify anomalies and normal nodes by not explicitly learning the data distribution,  therefore you propose to learn from the trajectories produced by the forward process. I don't see a strong motivation for data generation for detectors.
4. The training process for the whole is not clear. 
5.  DiffAD still needs sufficient data in order to train the model while anomalous nodes are usually very few.
Recommendation: 6: marginally above the acceptance threshold
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 3 good
Clarity: 3 good
reviews: 
Comments: This paper studies the application of the forward (add noise) and backward (denoise) processes of the diffusion model to graph anomaly detection, respectively. During forward diffusion, the authors exploited the more significant egonet dissimilarity change of the anomalies and proposed the use of LSTM/Transformer/MLP to capture this change information and perform anomaly detection. During backward diffusion, the authors proposed a graph generation method that recovers low-frequency energy as a data augmentation. Theoretical analyses and comprehensive experiments are performed.
Strengths: 1. The paper is well written and the charts are professional.

2. The exploration experiment is very detailed and the two propositions are well illustrated.

3. Substantial theoretical analyses and experimental results are provided.
Weaknesses: 1.  The paper's focus on a limited type of anomaly (contextual anomaly).

2.  While the analysis of the forward diffusion process is well-done, the proposed method (Approach I) lacks sufficient evidence to demonstrate its superiority.

3.  The experiments presented in the paper have some shortcomings that should be addressed.
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: 3 good
reviews: 
Comments: The authors propose a model for anomaly detection in graphs, particularly "contextual" anomalies, which are nodes whose attributes are significantly different from neighboring nodes. The main proposed idea is to use a denoising diffusion probabilistic model (DDPM) to learn a distribution of the graph that can be used to add/remove noise and generate synthetic graphs. The authors then use the DDPM in 2 different ways. First, they combine it with an LSTM to learn to classify nodes in a sequence of increasingly noise graphs as anomalous or normal. Second, they use the DDPM to generate synthetic graphs that can be used to improve predictive performance of other previously proposed models for anomaly detection on graphs.
Strengths: - Using a DDPM to model noise in a graph is interesting and avoids issues with over-smoothing when using graph neural networks for anomaly detection
- Preliminary study is insightful and effective at motivating the proposed methods
Weaknesses: - Limited experimental evaluation. No discussion of ablation or hyperparameter turning.
- No comparison to recently-proposed methods for graph anomaly detection, such as CoLA or CONAD. The justification is that such methods are not classifiers and the just assign an "anomaly score" to nodes. However, one could try a simple baseline of thresholding this anomaly score.
- No discussion of running time
- No discussion of inference for the DDPM
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 3 good
Clarity: 2 fair