Graph neural network-based address classification method for account balance model blockchain
To regulate the transactional activities on the public blockchain involving account balance models, it is necessary to conduct research on address classification for transactions on such blockchains.A blockchain address classification method, named AJKGS-ABCM (attention jumping knowledge graph SAGE...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2023-09-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023173/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841539200290127872 |
|---|---|
| author | Zhiyuan LI Binglei XU Yingyi ZHOU |
| author_facet | Zhiyuan LI Binglei XU Yingyi ZHOU |
| author_sort | Zhiyuan LI |
| collection | DOAJ |
| description | To regulate the transactional activities on the public blockchain involving account balance models, it is necessary to conduct research on address classification for transactions on such blockchains.A blockchain address classification method, named AJKGS-ABCM (attention jumping knowledge graph SAGE account-based blockchain classification model), was proposed to categorize blockchain addresses, providing effective support for blockchain transaction tracking.Blockchain transaction data was represented as a graph structure, with addressed as nodes and transactions as edges.The AJK-GraphSAGE algorithm was introduced to learn embedded representations of the graph, where the model’s input required only nodes and their sampled neighboring node sets.Simultaneously, attention mechanisms and skip-connection knowledge integration strategies were incorporated into the model, allowing for adaptive weight allocation across different layers and information sharing between various levels, thereby enhancing training speed and generalization capabilities.Finally, experimental comparisons are conducted, demonstrating superior performance in terms of accuracy, recall, and F1 score compared to other methods. |
| format | Article |
| id | doaj-art-6ae336ecc32147c490b47d334ec93664 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-6ae336ecc32147c490b47d334ec936642025-01-14T07:23:32ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-09-014411512659835988Graph neural network-based address classification method for account balance model blockchainZhiyuan LIBinglei XUYingyi ZHOUTo regulate the transactional activities on the public blockchain involving account balance models, it is necessary to conduct research on address classification for transactions on such blockchains.A blockchain address classification method, named AJKGS-ABCM (attention jumping knowledge graph SAGE account-based blockchain classification model), was proposed to categorize blockchain addresses, providing effective support for blockchain transaction tracking.Blockchain transaction data was represented as a graph structure, with addressed as nodes and transactions as edges.The AJK-GraphSAGE algorithm was introduced to learn embedded representations of the graph, where the model’s input required only nodes and their sampled neighboring node sets.Simultaneously, attention mechanisms and skip-connection knowledge integration strategies were incorporated into the model, allowing for adaptive weight allocation across different layers and information sharing between various levels, thereby enhancing training speed and generalization capabilities.Finally, experimental comparisons are conducted, demonstrating superior performance in terms of accuracy, recall, and F1 score compared to other methods.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023173/account balance model blockchainaddress classificationgraph neural networkattention mechanismjumping knowledge |
| spellingShingle | Zhiyuan LI Binglei XU Yingyi ZHOU Graph neural network-based address classification method for account balance model blockchain Tongxin xuebao account balance model blockchain address classification graph neural network attention mechanism jumping knowledge |
| title | Graph neural network-based address classification method for account balance model blockchain |
| title_full | Graph neural network-based address classification method for account balance model blockchain |
| title_fullStr | Graph neural network-based address classification method for account balance model blockchain |
| title_full_unstemmed | Graph neural network-based address classification method for account balance model blockchain |
| title_short | Graph neural network-based address classification method for account balance model blockchain |
| title_sort | graph neural network based address classification method for account balance model blockchain |
| topic | account balance model blockchain address classification graph neural network attention mechanism jumping knowledge |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023173/ |
| work_keys_str_mv | AT zhiyuanli graphneuralnetworkbasedaddressclassificationmethodforaccountbalancemodelblockchain AT bingleixu graphneuralnetworkbasedaddressclassificationmethodforaccountbalancemodelblockchain AT yingyizhou graphneuralnetworkbasedaddressclassificationmethodforaccountbalancemodelblockchain |