Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts
Authors: Chaoxi Niu, Hezhe Qiao, Changlu Chen, Ling Chen, Guansong Pang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. Code is available at https: //github.com/mala-lab/UNPrompt. |
| Researcher Affiliation | Academia | 1 AAII, University of Technology Sydney, Sydney, Australia 2 School of Computing and Information Systems, Singapore Management University, Singapore 3 Faculty of Data Science, City University of Macau, Macau, China |
| Pseudocode | No | The paper describes the methodology in prose, detailing components and their functions, but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Code is available at https: //github.com/mala-lab/UNPrompt. |
| Open Datasets | Yes | We evaluate UNPrompt on several real-world GAD datasets from diverse social networks, online shopping co-review networks, and co-purchase networks. Specifically, the social networks include Facebook [Xu et al., 2022], Reddit [Kumar et al., 2019] and Weibo [Kumar et al., 2019]. The co-review networks consist of Amazon [Mc Auley and Leskovec, 2013], Yelp Chi [Rayana and Akoglu, 2015], Amazon-all and Yelp Chi-all. Disney [S anchez et al., 2013] is a co-purchase network. |
| Dataset Splits | No | The paper states that UNPrompt is trained on Facebook and tested on other GAD datasets (zero-shot setting), but it does not specify explicit train/validation/test splits in terms of percentages or sample counts for the training dataset (Facebook) or any other dataset if it were used for direct training. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware components such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, such as programming languages, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | For a fair comparison, the common dimensionality is set to eight for all methods, and SVD is used for feature projection. The number of GNN layers is set to one and the number of hidden units is 128. The transformation layer is implemented as a one-layer MLP with the same number of hidden units. The size of the neighborhood prompt is set to one. Results for other hyperparameter settings are presented in the supplementary. For all baselines, their recommended optimization and hyperparameter settings are used. |