Network representation learning aims to learn the low-dimensional dense real-valued vector of network information

which solves practical tasks such as link prediction

anomaly detection

and recommendation systems.Recently

network representation learning has made great progress.Most existing researches focus on static networks

while real network is dynamic all the time.This survey proposes state of the arts on representation learning of dynamic network.Firstly

it provides historical overview of representation learning in network

followed by the motivation and theoretical basis of dynamic network representation learning.Then comprehensive analysis of dynamic models is proposed

including matrix factorization

random-walk

deep learning

edge reconstruction based dynamic models

and gives the application scenarios of dynamic network embedding.Finally

research directions of representation learning in the future are summarized.Only when considering the temporal dynamics

structure and content can we truly reflect the evolution of the real network and make network representation learning more valuable.