Graph neural network-based address classification method for account balance model blockchain

Zhiyuan LI; Binglei XU; Yingyi ZHOU

doi:10.11959/j.issn.1000-436x.2023173

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Graph neural network-based address classification method for account balance model blockchain

Papers | 更新时间：2024-06-06

- Graph neural network-based address classification method for account balance model blockchain
- Journal on Communications Vol. 44, Issue 9, Pages: 115-126(2023)
- 作者机构：
  
  1. 江苏大学计算机科学与通信工程学院，江苏镇江 212013
  2. 江苏省工业网络安全技术重点实验室，江苏镇江 212013
  3. 江苏省泛在数据智能感知与分析应用工程研究中心，江苏镇江 212013
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2020YFB1005503);The Natural Science Foundation of Jiangsu Province(BK20201415)
- DOI：10.11959/j.issn.1000-436x.2023173
  CLC： TP301
- Online First：2023-09，
  
  Published：25 September 2023
- 稿件说明：
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Zhiyuan LI, Binglei XU, Yingyi ZHOU. Graph neural network-based address classification method for account balance model blockchain[J]. Journal on Communications, 2023, 44(9): 115-126.
DOI：

Zhiyuan LI, Binglei XU, Yingyi ZHOU. Graph neural network-based address classification method for account balance model blockchain[J]. Journal on Communications, 2023, 44(9): 115-126. DOI： 10.11959/j.issn.1000-436x.2023173.

摘要

为了监管账户余额模型公链上的交易，有必要对该类区块链上的交易进行地址分类研究。基于此，提出了一种基于图神经网络的账户余额模型区块链地址分类方法（简称 AJKGS-ABCM）以实现区块链地址的分类，为区块链交易追踪提供有效的支持。该方法将区块链交易数据建模为图结构，以地址为节点，交易为边，提出AJK-GraphSAGE 算法学习图的嵌入表示，模型的输入只需要节点及其采样的邻居节点集合。同时，模型引入注意力机制及跳跃知识结合策略，自适应地为不同层的表示分配权重，并在不同层间共享信息，提高了训练速度和泛化能力。最后进行了实验对比，结果表明该模型在准确度、召回率和F1分数上性能优于其他方法。

Abstract

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.

关键词

Keywords

references

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