{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:14:30Z","timestamp":1783700070650,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea government (MSIT)","award":["2022-0-00704"],"award-info":[{"award-number":["2022-0-00704"]}]},{"name":"Korea government (MSIT)","award":["2021-0-00368"],"award-info":[{"award-number":["2021-0-00368"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, cybercrimes that exploit the anonymity of blockchain are increasing. They steal blockchain users\u2019 assets, threaten the network\u2019s reliability, and destabilize the blockchain network. Therefore, it is necessary to detect blockchain cybercriminal accounts to protect users\u2019 assets and sustain the blockchain ecosystem. Many studies have been conducted to detect cybercriminal accounts in the blockchain network. They represented blockchain transaction records as homogeneous transaction graphs that have a multi-edge. They also adopted graph learning algorithms to analyze transaction graphs. However, most graph learning algorithms are not efficient in multi-edge graphs, and homogeneous graphs ignore the heterogeneity of the blockchain network. In this paper, we propose a novel heterogeneous graph structure called an account-transaction graph, ATGraph. ATGraph represents a multi-edge as single edges by considering transactions as nodes. It allows graph learning more efficiently by eliminating multi-edges. Moreover, we compare the performance of ATGraph with homogeneous transaction graphs in various graph learning algorithms. The experimental results demonstrate that the detection performance using ATGraph as input outperforms that using homogeneous graphs as the input by up to 0.2 AUROC.<\/jats:p>","DOI":"10.3390\/s23010463","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T03:08:59Z","timestamp":1672628939000},"page":"463","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Graph Learning-Based Blockchain Phishing Account Detection with a Heterogeneous Transaction Graph"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9347-3680","authenticated-orcid":false,"given":"Jaehyeon","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Applied Artificial Intelligence, Major in Bio-Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9021-7008","authenticated-orcid":false,"given":"Sejong","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Major in Bio-Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0978-3657","authenticated-orcid":false,"given":"Yushin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Major in Bio-Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8498-4577","authenticated-orcid":false,"given":"Seyoung","family":"Ahn","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Major in Bio-Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1847-6088","authenticated-orcid":false,"given":"Sunghyun","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hanyang University, Ansan 15588, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1109\/TNSE.2020.3005678","article-title":"Kirti: A blockchain-based credit recommender system for financial institutions","volume":"8","author":"Patel","year":"2020","journal-title":"IEEE Trans. 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