Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts

• URL: http://arxiv.org/abs/2410.14886v1
• Date: Fri, 18 Oct 2024 22:23:59 GMT
• Title: Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts
• Authors: Chaoxi Niu, Hezhe Qiao, Changlu Chen, Ling Chen, Guansong Pang,
• Abstract summary: Graph anomaly detection (GAD) aims to identify nodes in a graph that significantly deviate from normal patterns. Existing GAD methods, whether supervised or unsupervised, are one-model-for-one-dataset approaches. We propose a novel zero-shot generalist GAD approach UNPrompt that trains a one-for-all detection model.
• Score: 21.05107001235223
• License:
• 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. 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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