Bidirectional fusion heterogeneous graph networks for semi-supervised Bitcoin transaction anomaly detection in dynamic transaction graphs.
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| Title: | Bidirectional fusion heterogeneous graph networks for semi-supervised Bitcoin transaction anomaly detection in dynamic transaction graphs. |
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| Authors: | Xiao B; School of Economics and Management, Southeast University, Nanjing, China., Yin W; School of Economics and Management, Southeast University, Nanjing, China.; The Laboratory of Philosophy and Social Sciences at Universities in Jiangsu Province-Fintech and Big Data Laboratory of Southeast University, Nanjing, China. |
| Source: | PloS one [PLoS One] 2026 Jun 08; Vol. 21 (6), pp. e0351051. Date of Electronic Publication: 2026 Jun 08 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0351051 |
