Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts
graph anomaly detection;generalist model;prompt
Yes
No ethics review needed.
Can the authors clarify the specific differences between the "ZERO-SHOT" methodology presented in this paper and established inductive settings in graph neural networks?
1. The focus on graph anomaly detection is timely and relevant, as it addresses an important area within machine learning and data analysis.
2. The approach of training on one graph and testing on others is interesting, offering a perspective that could lead to valuable insights in diverse applications.
The paper proposes a method for Graph Anomaly Detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns. The GAD framework is trained on one graph and tested on other graphs, addressing a critical challenge in the detection of anomalies in graph-structured data.
Although the authors refer to their method as "ZERO-SHOT," it appears to be closely related to the established concept of "inductive setting" in graph neural networks, which involves using a number of complete graphs as training data and applying the model to completely unseen graphs for testing [1]. The distinction between the "ZERO-SHOT" defined in this paper and the inductive settings is not clear.

There are various definitions of "inductive setting". Some papers focus on learning from a training graph that includes only a portion of the nodes and making predictions for new nodes that were not part of the training set, while others examine completely different graphs (complete inductive). If the proposed method is effectively the same as the complete inductive setting, then using "ZERO-SHOT" to define their approach may not be accurate.

It is possible that the complete inductive setting has not been explored in the context of anomaly detection, which could represent a contribution of the paper. However, the authors should undertake a major revision, particularly in the introduction, related work, and comparison methods sections, to better articulate this contribution.

If the authors can clarify that "ZERO-SHOT" and "inductive settings" are indeed different, I would be willing to raise my score.

[1] Xu, Keyulu, et al. "Representation learning on graphs with jumping knowledge networks." International Conference on Machine Learning, 2018.
Yes
No ethics review needed.
The authors are expected to answer the questions listed in Weaknesses. Apart from these, I have the following questions:
1. As ARC (Liu et al 2024) is another generalist GAD method, can you further discuss the difference between UNPrompt and ARC? Is that possible to compare them together (even though their settings are slightly different)?
2. The "attribute predictability" looks similar to the "local affinity" in TAM (Qiao and Pang 2023). In this case, will the performance drop significantly if we replace this design with local affinity?
1. The research problem of zero-shot generalist GAD is both challenging and intriguing, offering the potential to inspire further innovation within the GAD research community.
2. The proposed method is thoughtfully designed with a balance of sophistication and simplicity, making it accessible for future researchers to build upon.
3. Extensive experiments conducted across diverse scenarios demonstrate the practical effectiveness and versatility of the proposed approach.
This paper investigates the zero-shot generalist graph anomaly detection (GAD) problem, aiming to train a one-for-all model that works on all potential datasets. A new method termed UNPrompt with several designs (including prompt tuning, attribute predictability, etc.) is provided. Experiments on both generalist and traditional settings are conducted to show the effectiveness of the proposed method.
1. The experimental details are not given clearly in the paper. Which graph datasets are used to train the model (for pre-training and/or prompt tuning)? 
2. In the experiments, only 4 different datasets are used for evaluation (Amazon-all and YelpChi-all can be regarded as an extended version of the original ones). In this case, the generalized ability of the proposed method can't be fully verified. In this case, more datasets from different domains are expected in the experiments.
3. Why coordinate-wise normalization plays a critical role in the performance of UNPrompt? More explanation is expected.
Yes
No ethics review needed.
Q1. Can the pre-trained model use a different dataset other than Facebook to train and still get good performance? 

Q2. Can the framework be used for large-scale datasets, like Elliptic[1]? 

Q3. Can UNPrompt still outperform other baselines after they use the Coordinate-wise Normalization technique? 


Reference: 

1. Jianheng Tang, Fengrui Hua, Ziqi Gao, Peilin Zhao, Jia Li. GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection. NeurIPS 2023. 
2. Jingyan Chen, Guanghui Zhu, Chunfeng Yuan, Yihua Huang. Boosting Graph Anomaly Detection with Adaptive Message Passing. ICLR 2024.
S1. The paper is easy to follow. 

S2. The authors propose a zero-shot generalist GAD approach including two main components, Coordinate-wise Normalization and neighborhood prompt learning. 

S3. Experiments show their framework can outperform baselines on datasets included in this paper.
The authors propose a zero-shot generalist GAD approach, UNPrompt. Specifically, they introduce two components: coordinate-wise normalization to unify node attributes across different graphs, and neighborhood prompt learning, which leverages latent node attribute predictability as an anomaly measure. Experiments show their framework can outperform baselines on datasets included in this paper.
W1. The Coordinate-wise Normalization component is hardly considered as a contribution in this paper. As stated in the paper, the authors simply follow the previous work in the related areas to apply the Coordinate-wise Normalization, so the authors need to explain the differences between their design and framework from others. Otherwise, this component can only be considered as an implementation, not a contribution. 

W2. The Latent Node Attribute Predictability is similar to Local Affinitiy in TAM. This anomalous score function shares a similar form of Local Affinitiy in TAM. Although the authors provide a comparison in Figure 3(b), they need to further explain the result. To be specific, changing the objective function in an unsupervised learning framework but not tuning hyperparameters carefully is hard to lead to optimal results. The authors need to illustrate whether they use the same hyperparameters as Latent Node Attribute Predictability for Local Affinitiy or they fine-tune optimal hyperparameters for Local Affinitiy in the comparison. 
W3. The Neighborhood Prompting Learning follows the previous framework. Graph prompt learning has been utilized in several downstream applications, but the authors still follow the typical design of it. They need to further explain the differences between their design and other graph prompt learning frameworks to show their contributions. Otherwise, this key component can have the same issue as W1. 

W4. The setting of the hyperparameters is not explained well. For example, for the unsupervised setting of UNPrompt, the authors use the "40th percentile of the normal scores" as the threshold to generate pseudo labels. However, the real anomalous rate is far less than this, which leads to many false pseudo-labels in this setting. They need to explain why they chose such a hyperparameter. Besides, they only provide the hyperparameter sensitivity of the number of prompts. It would be better to include the analysis of other hyperparameters. 

W5. The datasets and baselines are not diverse enough. For datasets, the authors only include "social networks" and "co-review", which may share similar structural information. It would be better to include financial networks, like T-Finance[1]. For baselines, the most recent supervised work included in this paper is GHRN and unsupervised work is TAM, but XGBGraph[1] and GADAM[2] are two novel works in this area, separately. In addition, since Coordinate-wise Normalization is a very common design when compared with baselines, it would be better to include such a technique for baseline models.
Yes
None
No ethics review needed.
1. What are the unique challenges of prompt learning in Graph Anomaly Detection (GAD)?
2.  In Formula 2, the mean value will also be large when the difference in features between the two datasets is large. So, how can the two data be projected to the same space?
3. How are potential negative values from Formula 2 handled?
4. Formulas 4 and 5 suggest that the authors only consider graph uniformity or high node connectivity as criteria for determining anomalies. This design seems somewhat superficial, as anomalous nodes may also be related to node attributes.
5. Does Neighborhood Prompting involve learning a prompt for each node's neighbours? If the added prompts alter the neighbour features of *x*, could this affect the detection of anomalies?
1. The experiments are detailed and demonstrate strong performance.
2. The paper is well-organized.
3. The work addresses a novel problem in a new context: zero-shot Graph Anomaly Detection (GAD).
This paper proposes a novel zero-shot generalist Graph Anomaly Detection (GAD) approach called UNPrompt, which aims to train a unified detection model. The approach achieves generalist GAD through two key modules: the first module aligns the dimensionality and semantics of node attributes across different graphs by employing coordinate-wise normalization in a projected space. The second module focuses on learning generalized neighborhood prompts that leverage the predictability of latent node attributes as an anomaly score across various datasets.
1. The theoretical innovation is insufficient. The concept of Neighborhood Prompting appears to encompass an all-in-one method in [1], and the method of coordinate-wise normalization is not novel. The paper does not sufficiently address the unique challenges associated with applying these methods to GAD.
2. The consideration of graph anomalies is inadequate. The evaluation focuses primarily on graph uniformity or high node connectivity as criteria for determining anomalies, while neglecting the importance of attribute features.
3. The writing needs improvement; the paper contains too many lengthy sentences, making it difficult to read and understand.
4. No code provided

   [1] Sun, Xiangguo, et al. "All in one: Multi-task prompting for graph neural networks." *Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining*. 2023.
@inproceedings{
anonymous2024zeroshot,
title={Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=ZslV4L5AhM},
note={under review}
}
Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. Existing GAD methods, whether supervised or unsupervised, are one-model-for-one-dataset approaches, i.e., training a separate model for each graph dataset. This limits their applicability in real-world scenarios where training on the target graph data is not possible due to issues like data privacy. To overcome this limitation, we propose a novel zero-shot generalist GAD approach **UNPrompt** that trains a one-for-all detection model, requiring the training of one GAD model on a single graph dataset and then effectively generalizing to detect anomalies in other graph datasets without any retraining or fine-tuning. The key insight in UNPrompt is that i) the predictability of latent node attributes can serve as a generalized anomaly measure and ii)  highly generalized normal and abnormal graph patterns can be learned via latent node attribute prediction in a properly normalized node attribute space. UNPrompt achieves generalist GAD through two main modules: one module aligns the dimensionality and semantics of node attributes across different graphs via coordinate-wise normalization in a projected space, while another module learns generalized neighborhood prompts that support the use of latent node attribute predictability as an anomaly score across different datasets. Extensive experiments on real-world GAD datasets show that UNPrompt significantly outperforms diverse competing methods under the generalist GAD setting, and it also has strong superiority under the one-model-for-one-dataset setting.
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Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts
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