{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:42:57Z","timestamp":1780764177512,"version":"3.54.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/359","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"3226-3234","source":"Crossref","is-referenced-by-count":3,"title":["Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts"],"prefix":"10.24963","author":[{"given":"Chaoxi","family":"Niu","sequence":"first","affiliation":[{"name":"University of Technology Sydney"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hezhe","family":"Qiao","sequence":"additional","affiliation":[{"name":"Singapore Management University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changlu","family":"Chen","sequence":"additional","affiliation":[{"name":"City University of Macau"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Technology Sydney"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guansong","family":"Pang","sequence":"additional","affiliation":[{"name":"Singapore Management University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:48Z","timestamp":1758627228000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/359"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/359","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}