  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
