论文标题
在加密货币事务网络中的时间链接预测的图形正则非负潜在因子分析模型
Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks
论文作者
论文摘要
随着区块链技术的发展，基于区块链技术的加密货币越来越受欢迎。这给出了一个巨大的加密货币交易网络，引起了广泛关注。网络的链接预测学习结构有助于了解网络的机制，因此在加密货币网络中也广泛研究了网络的机制。但是，过去研究中忽略了加密货币交易网络的动态。我们使用图形正则方法将过去的交易记录与未来交易联系起来。基于此，我们提出了一种潜在因子依赖性，非负因子，乘法和图形正规化的已归合性更新（SLF-NMGRU）算法，并进一步提出了图形正则化的非负潜在因子分析（GRNLFA）模型。最后，在真实的加密货币交易网络上进行的实验表明，提出的方法提高了准确性和计算效率
With the development of blockchain technology, the cryptocurrency based on blockchain technology is becoming more and more popular. This gave birth to a huge cryptocurrency transaction network has received widespread attention. Link prediction learning structure of network is helpful to understand the mechanism of network, so it is also widely studied in cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in the past researches. We use graph regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, non-negative, multiplicative and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose graph regularized nonnegative latent factor analysis (GrNLFA) model. Finally, experiments on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and the computational efficiency
