基于图神经网络的账户余额模型区块链地址分类方法

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

Abstract

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.

Key words: account balance model blockchain, address classification, graph neural network, attention mechanism, jumping knowledge

CLC Number:

TP301

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.

Figures/Tables 13

参数	含义
N	节点的数量
$A$	图的邻接矩阵，其中A_ij 表示节点i和节点 j之间是否有边
$X$	节点特征矩阵，其中 $x_{i}$ 表示节点i的特征向量
k	GraphSAGE的层数，k=1,2,…,K，K为总层数
C	分类类别的数量，其中c=1,2,…,C表示每个类别
Θ	模型参数集合，包括GraphSAGE层的权重矩阵 $W^{1}, W^{2}, \dots, W^{K}$ 、注意力权重矩阵 $W_{a}$ 和分类层权重矩阵 $W_{c}$
η	学习率，用于随机梯度下降算法
$h_{i}^{k}$	节点v_i 在第k层的表示
$N (v_{i})$	节点v_i 的邻居节点集合
$H_{}^{1}, H_{}^{2}, \dots, H_{}^{K}$	经过K个GraphSAGE层的输出表示矩阵
$A_{_{i}}^{k}$	节点v_i 的注意力权重向量，表示第k层GraphSAGE层输出表示的重要性
$H_{i}^{final}$	节点v_i 经过注意力和跳跃知识结合策略处理后的最终表示
O	分类层的输出
$P$	类别概率矩阵，其元素P_i,c表示节点v_i 属于类别c 的概率
$Y$	节点的真实类别标签矩阵，表示每个节点的独热编码类别标签
L	交叉熵损失函数值，用于衡量模型预测与真实类别标签之间的一致性
$\nabla_{Θ} L$	损失函数关于模型参数的梯度

指标	计算式	含义
Precision	$P = \frac{TP}{TP+FP}$	被正确分类为某标签的地址占总的被分类为某标签的地址的比例
Recall	$R (TPR) = \frac{TP}{TP+FN}$	所有正例中被正确分类的比例
F1-score	$F1-score = 2 \frac{P R}{P + R}$	兼顾Precision和Recall的指标
micro-F1	$micro-F1 = \frac{\sum_{i = 1}^{C} 2 {TP}_{i}}{\sum_{i = 1}^{C} 2 {TP}_{i} + {FP}_{i} + {FN}_{i}}$	先计算所有类别的总的Precision和Recall，后计算F1值，即micro-F1
macro-F1	$macro-F1 = \frac{1}{C} \sum_{i = 1}^{C} \frac{2 {TP}_{i}}{2 {TP}_{i} + {FP}_{i} + {FN}_{i}}$	将所有类别的Precision和Recall求平均，然后计算F1值，即macro-F1

References 23

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标签名称	标签符号	标签描述
交易所	Exchange	该地址属于加密货币交易所，可能涉及加密货币的买卖交易和其他金融服务
矿池/矿工	Mining_pool/Mining	该地址是由一个或多个矿工汇集成的矿池地址，可能涉及加密货币挖矿等活动
博彩	Gambling	该地址可能是在线博彩网站的地址，可能涉及加密货币博彩活动
合约代币	Token	该地址属于一个以太坊代币合约，可能用于代币发行、代币交易等活动
暗网交易	Darknet	该地址可能是一个用于在暗网进行加密货币交易的地址，可能涉及非法交易
钱包	Wallet	该地址可能属于一个以太坊钱包，可能用于存储、管理和转移加密货币
欺诈	Scam	该地址可能是用于进行欺诈活动的地址，可能涉及虚假交易、社工钓鱼、伪造代币等活动
筹资活动	Ico	该地址可能是用于Ico或其他筹资活动的地址，可能涉及投资、筹款等活动
黑客	Hacker	该地址可能是用于黑客盗币活动的地址，可能涉及攻击以太坊网络、其他区块链网络或其他网络的活动
货币清洗	Mixer	该地址可能是用于混合加密货币交易的地址，可能涉及加密货币的匿名化处理
普通用户	User	该地址属于一般的以太坊用户，可能仅用于普通的加密货币交易等活动

方法	Exchange	Mining	Gambling	Token	Darknet	Wallet	Scam	Ico	Hacker	Mixer	User
KNN	0.683	0.675	0.651	0.643	0.648	0.656	0.651	0.624	0.638	0.671	0.661
Deepwalk	0.722	0.715	0.708	0.707	0.701	0.725	0.695	0.727	0.711	0.733	0.728
Node2Vec	0.735	0.731	0.715	0.722	0.719	0.731	0.718	0.734	0.724	0.740	0.723
Struc2Vec	0.741	0.735	0.724	0.731	0.730	0.745	0.729	0.738	0.733	0.744	0.739
I2BGNN	0.831	0.812	0.819	0.826	0.822	0.842	0.833	0.840	0.832	0.829	0.841
GCN	0.821	0.812	0.810	0.809	0.791	0.822	0.811	0.819	0.801	0.820	0.815
GraphSAGE	0.843	0.821	0.819	0.826	0.815	0.833	0.825	0.838	0.841	0.845	0.835
AJKGS-ABCM	0.875	0.856	0.845	0.861	0.855	0.871	0.854	0.870	0.873	0.881	0.859

方法	Exchange	Mining	Gambling	Token	Darknet	Wallet	Scam	Ico	Hacker	Mixer	User
KNN	0.665	0.661	0.642	0.635	0.638	0.649	0.638	0.619	0.626	0.655	0.648
Deepwalk	0.715	0.720	0.695	0.698	0.688	0.714	0.681	0.709	0.701	0.716	0.715
Node2Vec	0.712	0.720	0.705	0.709	0.706	0.722	0.703	0.718	0.709	0.729	0.716
Struc2Vec	0.733	0.718	0.715	0.722	0.719	0.737	0.721	0.726	0.712	0.729	0.715
I2BGNN	0.823	0.809	0.811	0.815	0.806	0.818	0.825	0.836	0.814	0.811	0.829
GCN	0.811	0.806	0.801	0.795	0.785	0.807	0.803	0.812	0.795	0.808	0.802
GraphSAGE	0.831	0.809	0.812	0.811	0.803	0.818	0.814	0.818	0.825	0.831	0.817
AJKGS-ABCM	0.855	0.841	0.838	0.852	0.836	0.862	0.846	0.857	0.861	0.870	0.849

方法	Exchange	Mining	Gambling	Token	Darknet	Wallet	Scam	Ico	Hacker	Mixer	User
KNN	0.674	0.668	0.646	0.639	0.643	0.652	0.644	0.622	0.632	0.663	0.654
Deepwalk	0.718	0.710	0.702	0.703	0.694	0.719	0.688	0.718	0.706	0.724	0.721
Node2Vec	0.723	0.725	0.710	0.716	0.712	0.726	0.710	0.726	0.716	0.734	0.719
Struc2Vec	0.737	0.726	0.719	0.726	0.724	0.741	0.725	0.732	0.722	0.736	0.727
I2BGNN	0.827	0.816	0.815	0.820	0.814	0.830	0.829	0.838	0.823	0.820	0.835
GCN	0.816	0.809	0.805	0.802	0.788	0.814	0.807	0.816	0.798	0.814	0.808
GraphSAGE	0.837	0.815	0.816	0.818	0.825	0.825	0.819	0.828	0.833	0.838	0.826
AJKGS-ABCM	0.865	0.849	0.841	0.856	0.866	0.866	0.850	0.864	0.867	0.875	0.854

方法	Precision	Recall	micro-F1	macro-F1
KNN	0.655	0.643	0.645	0.648
DeepWalk	0.716	0.703	0.703	0.709
Node2Vec	0.725	0.713	0.715	0.719
Struc2Vec	0.734	0.722	0.725	0.727
I2BGNN	0.83	0.819	0.825	0.824
GCN	0.811	0.802	0.801	0.807
GraphSAGE	0.828	0.819	0.818	0.823
AJKGS-ABCM	0.862	0.852	0.849	0.856

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

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