Graph neural network-based transaction link prediction method for public blockchain in heterogeneous information networks
Public blockchain has outstanding performance in transaction privacy protection because of its anonymity. The data openness brings feasibility to transaction behavior analysis. At present, the transaction data of the public chain are huge, including complex trading objects and relationships. It is d...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Blockchain: Research and Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096720924000782 |
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| author | Zening Zhao Jinsong Wang Jiajia Wei |
| author_facet | Zening Zhao Jinsong Wang Jiajia Wei |
| author_sort | Zening Zhao |
| collection | DOAJ |
| description | Public blockchain has outstanding performance in transaction privacy protection because of its anonymity. The data openness brings feasibility to transaction behavior analysis. At present, the transaction data of the public chain are huge, including complex trading objects and relationships. It is difficult to extract attributes and predict transaction behavior by traditional methods. To solve these problems, we extract transaction features to construct an Ethereum transaction heterogeneous information network (HIN) and propose a graph neural network (GNN)-based transaction prediction method for public blockchains in HINs, which can divide the network into subgraphs according to connectivity and increase the accuracy of the prediction results of transaction behavior. Experiments show that the execution time consumption of the proposed transaction subgraph division method is reduced by 70.61% on average compared with that of the search method. The accuracy of the proposed behavior prediction method also improves compared with that of the traditional random walk method, with an average accuracy of 83.82%. |
| format | Article |
| id | doaj-art-d5f1d8bc8df046618c328a8ec243e2a6 |
| institution | Kabale University |
| issn | 2666-9536 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Blockchain: Research and Applications |
| spelling | doaj-art-d5f1d8bc8df046618c328a8ec243e2a62025-08-20T03:32:12ZengElsevierBlockchain: Research and Applications2666-95362025-06-016210026510.1016/j.bcra.2024.100265Graph neural network-based transaction link prediction method for public blockchain in heterogeneous information networksZening Zhao0Jinsong Wang1Jiajia Wei2School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China; Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin 300384, China; National Engineering Laboratory for Computer Virus Prevention and Control Technology, Tianjin 300457, ChinaSchool of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China; Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin 300384, China; National Engineering Laboratory for Computer Virus Prevention and Control Technology, Tianjin 300457, China; Corresponding author.School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China; Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin 300384, China; National Engineering Laboratory for Computer Virus Prevention and Control Technology, Tianjin 300457, ChinaPublic blockchain has outstanding performance in transaction privacy protection because of its anonymity. The data openness brings feasibility to transaction behavior analysis. At present, the transaction data of the public chain are huge, including complex trading objects and relationships. It is difficult to extract attributes and predict transaction behavior by traditional methods. To solve these problems, we extract transaction features to construct an Ethereum transaction heterogeneous information network (HIN) and propose a graph neural network (GNN)-based transaction prediction method for public blockchains in HINs, which can divide the network into subgraphs according to connectivity and increase the accuracy of the prediction results of transaction behavior. Experiments show that the execution time consumption of the proposed transaction subgraph division method is reduced by 70.61% on average compared with that of the search method. The accuracy of the proposed behavior prediction method also improves compared with that of the traditional random walk method, with an average accuracy of 83.82%.http://www.sciencedirect.com/science/article/pii/S2096720924000782BlockchainLink predictionHeterogeneous information networksGraph neural networksRandom walk |
| spellingShingle | Zening Zhao Jinsong Wang Jiajia Wei Graph neural network-based transaction link prediction method for public blockchain in heterogeneous information networks Blockchain: Research and Applications Blockchain Link prediction Heterogeneous information networks Graph neural networks Random walk |
| title | Graph neural network-based transaction link prediction method for public blockchain in heterogeneous information networks |
| title_full | Graph neural network-based transaction link prediction method for public blockchain in heterogeneous information networks |
| title_fullStr | Graph neural network-based transaction link prediction method for public blockchain in heterogeneous information networks |
| title_full_unstemmed | Graph neural network-based transaction link prediction method for public blockchain in heterogeneous information networks |
| title_short | Graph neural network-based transaction link prediction method for public blockchain in heterogeneous information networks |
| title_sort | graph neural network based transaction link prediction method for public blockchain in heterogeneous information networks |
| topic | Blockchain Link prediction Heterogeneous information networks Graph neural networks Random walk |
| url | http://www.sciencedirect.com/science/article/pii/S2096720924000782 |
| work_keys_str_mv | AT zeningzhao graphneuralnetworkbasedtransactionlinkpredictionmethodforpublicblockchaininheterogeneousinformationnetworks AT jinsongwang graphneuralnetworkbasedtransactionlinkpredictionmethodforpublicblockchaininheterogeneousinformationnetworks AT jiajiawei graphneuralnetworkbasedtransactionlinkpredictionmethodforpublicblockchaininheterogeneousinformationnetworks |