---
title: "Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts"
authors:
  - "Chaoxi Niu"
  - "Hezhe Qiao"
  - "Changlu Chen"
  - "Ling Chen"
  - "Guansong Pang"
doi: "10.24963/ijcai.2025/359"
url: "https://ijcai.org/proceedings/2025/359"
pdf: "https://ijcai.org/proceedings/2025/0359.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "3226-3234"
track: "Main Track"
keywords:
  - "Data Mining: DM: Mining graphs"
  - "Data Mining: DM: Anomaly/outlier detection"
---

# Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts

Chaoxi Niu, Hezhe Qiao, Changlu Chen, Ling Chen, Guansong Pang

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 3226-3234. DOI: [10.24963/ijcai.2025/359](https://doi.org/10.24963/ijcai.2025/359)

## Abstract

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. However, existing GAD methods are one-model-for-one-dataset approaches, i.e., training a separate model for each graph dataset. This largely limits their applicability in real-world scenarios. 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) generalized normal and abnormal graph patterns can be learned via latent node attribute prediction in a properly normalized node attribute space. UNPrompt achieves a generalist mode for GAD through two main modules: one module aligns the dimensionality and semantics of node attributes across different graphs via coordinate-wise normalization, 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. Code is available at https://github.com/mala-lab/UNPrompt.