{"type":"ScholarlyArticle","title":"Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection","authors":[{"name":"Runang He","slug":"runang-he","given":"Runang","family":"He","affiliation":""},{"name":"Tongya Zheng","slug":"tongya-zheng","given":"Tongya","family":"Zheng","affiliation":""},{"name":"Huiling Peng","slug":"huiling-peng","given":"Huiling","family":"Peng","affiliation":""},{"name":"Yuanyu Wan","slug":"yuanyu-wan","given":"Yuanyu","family":"Wan","affiliation":""},{"name":"Bingde Hu","slug":"bingde-hu","given":"Bingde","family":"Hu","affiliation":""},{"name":"Jiawei Chen","slug":"jiawei-chen","given":"Jiawei","family":"Chen","affiliation":""},{"name":"Canghong Jin","slug":"canghong-jin","given":"Canghong","family":"Jin","affiliation":""},{"name":"Mingli Song","slug":"mingli-song","given":"Mingli","family":"Song","affiliation":""},{"name":"Can Wang","slug":"can-wang","given":"Can","family":"Wang","affiliation":""}],"abstract":"Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomaly Detection (GAD) approaches applied to blockchains have faced two critical challenges: adversarial pattern evolution by malicious actors and the out-of-distribution (OOD) problem caused by varied transaction semantics on blockchains. To address these challenges, we propose a novel framework termed TEmporal Motif-aware Graph Test-Time Adaptation (TEMG-TTA). First, we comprehensively capture the 3-node temporal motif distribution of each active address using an efficient computational mechanism, enabling downstream temporal motif-aware graph learning. Second, we design a simple yet effective test-time adaptation strategy to facilitate the sharing of common patterns between training and testing graphs. Extensive experiments on 5 real-world datasets demonstrate that our proposed TEMG-TTA outperforms state-of-the-art GAD approaches by an average of 54.88%. A further case study on interpretable motif patterns reveals that TEMG-TTA explicitly characterizes the complex transaction patterns of anomalous addresses, thereby verifying the effectiveness of our technical designs. Our code is publicly available at https://github.com/LuoXishuang0712/TEMG-TTA/.","doi":"10.24963/ijcai.2026/802","url":"https://ijcai.org/proceedings/2026/802","pdf":"https://ijcai.org/proceedings/2026/0802.pdf","markdown":"https://ijcai.org/proceedings/2026/802.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/802","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Social Good","track":"AI and Social Good","number":802,"pages":{"first":7210,"last":7218},"keywords":["Data Mining: Data Mining","Machine Learning: Machine Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}