reviews: 
Comments: The paper investigates the problem of graph anomaly detection in a semi-supervised setting where anomalous nodes tend to differ from their neighbors in terms of the distribution of node features (not structural differences). Under the assumption that the mean attribute values are the same across anomalous and non-anomalous nodes, the authors prove that in a step-wise diffusion process that adds incremental noise to the attribute matrix X, the egonet dissimilarity $\Omega = LX$ for nodes that have higher variance attributes will change more drastically than for those which have lower variance. Based on this observation, the authors propose (non-GNN) neural models that learn from the sequence of egonet dissimilarities to detect anomalies. Moreover, they prove that the relative accumulated energy present in low frequency signals decreases as X gets increasingly corrupted. In light of this observation, they propose a denoising network that utilizes one GCN (low-pass filter) per step. This allows them to generate additional attribute vectors for each node based on their fully-corrupted counterparts. The proposed model, DiffAD, is evaluated on six datasets w.r.t. Macro-F1 and AUC and compared with four graph anomaly detection baselines. In another set of experiments, they show that the generated attribute matrices X bring the performance of GraphSAGE very close to that of DiffAD.
Strengths: S1. Proposes an alternative to GNN-based solutions for detecting anomalous nodes in a graph (which has applications related to spam and fraud detection).

S2. Proposed method, DiffAD, outperforms recent baselines on six datasets.

S3. Proposed graph generation technique leads to substantial performance improvements when used for training classic GNN methods.

S4. The proposed approaches are designed based on the theoretical principles proven in the paper.

S5. The overall writing quality is great, the text is easy to follow and the appendices are used in a thoughtful way.

S6. Overall reproducibility is high; code is also provided (in an anonymous github repo).
Weaknesses: W1. In the preliminary study setup, the attribute distribution chosen for anomalous nodes is substantially different from that of the normal nodes. When noise is incrementally added through diffusion steps, it is not surprising that the egonet dissimilarity drops faster for anomalous nodes.

W2. The authors show that this rapid egonet dissimilarity decrease holds for CORA dataset, but the anomalies in the data were synthetically injected by Liu et al., 2022. For organic anomalies, this assumption might also be true on average (Liu et al., 2022), but the fact that DiffAD  does not account for structural anomalies will likely prevent it from detecting anomalies if their initial egonet dissimilarity is low.

W3. It is not clear how to extend these principles to consider for structural anomalies. The paper dismisses the need to consider structural anomalies that "form more densely [connected] links with other nodes", but there could be anomalies that also keep the density somewhat unchanged.

W4. Some potential typos; some word choices can be improved.
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: 2 fair
Clarity: 3 good