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Transaction Prediction in Blockchain: A Negative Link Prediction Algorithm Based on the Sentiment Analysis and Balance Theory
Wireless Communications and Mobile Computing Pub Date : 2021-02-16 , DOI: 10.1155/2021/8871150
Ling Yuan 1 , JiaLi Bin 1 , YinZhen Wei 2 , Zhihua Hu 2 , Ping Sun 2
Affiliation  

User relationship prediction in the transaction of Blockchain is to predict whether a transaction will occur between two users in the future, which can be abstracted into the link prediction problem. The link prediction can be categorized into the positive one and the negative one. However, the existing negative link prediction algorithms mainly consider the number of negative user interactions and lack the full use of emotion characteristics in user interactions. To solve this problem, this paper proposes a negative link prediction algorithm based on the sentiment analysis and balance theory. Firstly, the user interaction matrix is constructed based on calculating the intensity of emotion polarity for social network texts, and a reliability weight matrix (noted as RW-matrix) is constructed based on the user interaction matrix to measure the reliability of negative links. Secondly, with the RW-matrix, a negative link prediction algorithm is proposed based on the structural balance theory by constructing negative link sample sets and extracting sample features. To evaluate the performance of the negative link prediction algorithm proposed, the variable management method is used to analyze the influence of negative sample control error and other parameters on the accuracy of it. Compared with the existing prediction benchmark algorithms, the experimental results demonstrate that the proposed negative link prediction algorithm can improve the accuracy of prediction significantly and deliver good performances.

中文翻译:

区块链中的交易预测:基于情感分析和平衡理论的负链接预测算法

区块链交易中的用户关系预测是预测未来两个用户之间是否会发生交易,可以抽象为链接预测问题。链接预测可以分为积极的和消极的。然而,现有的否定链接预测算法主要考虑否定用户交互的数量,并且在用户交互中缺乏情感特征的充分利用。为了解决这个问题,本文提出了一种基于情感分析和平衡理论的负面链接预测算法。首先,在计算社交网络文本的情感极性强度的基础上,构建用户交互矩阵,在用户交互矩阵的基础上,构造了可靠性权重矩阵(记为RW矩阵),以度量负链接的可靠性。其次,在RW矩阵的基础上,通过构造负链接样本集并提取样本特征,提出了一种基于结构平衡理论的负链接预测算法。为了评估所提出的负链接预测算法的性能,使用变量管理方法分析了负样本控制误差和其他参数对其准确性的影响。与现有的预测基准算法相比,实验结果表明,提出的负链接预测算法可以显着提高预测的准确性,并具有良好的性能。通过构造负链接样本集并提取样本特征,提出一种基于结构平衡理论的负链接预测算法。为了评估所提出的负链接预测算法的性能,使用变量管理方法分析了负样本控制误差和其他参数对其准确性的影响。与现有的预测基准算法相比,实验结果表明,所提出的负链接预测算法可以显着提高预测的准确性,并具有良好的性能。通过构造负链接样本集并提取样本特征,提出一种基于结构平衡理论的负链接预测算法。为了评估所提出的负链接预测算法的性能,使用变量管理方法分析了负样本控制误差和其他参数对其准确性的影响。与现有的预测基准算法相比,实验结果表明,提出的负链接预测算法可以显着提高预测的准确性,并具有良好的性能。变量管理方法用于分析负样本控制误差和其他参数对其准确性的影响。与现有的预测基准算法相比,实验结果表明,提出的负链接预测算法可以显着提高预测的准确性,并具有良好的性能。变量管理方法用于分析负样本控制误差和其他参数对其准确性的影响。与现有的预测基准算法相比,实验结果表明,提出的负链接预测算法可以显着提高预测的准确性,并具有良好的性能。
更新日期:2021-02-16
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