  Network embedding is a very important method for network data. However, most
of the algorithms can only deal with static networks. In this paper, we propose
an algorithm Recurrent Neural Network Embedding (RNNE) to deal with dynamic
network, which can be typically divided into two categories: a) topologically
evolving graphs whose nodes and edges will increase (decrease) over time; b)
temporal graphs whose edges contain time information. In order to handle the
changing size of dynamic networks, RNNE adds virtual node, which is not
connected to any other nodes, to the networks and replaces it when new node
arrives, so that the network size can be unified at different time. On the one
hand, RNNE pays attention to the direct links between nodes and the similarity
between the neighborhood structures of two nodes, trying to preserve the local
and global network structure. On the other hand, RNNE reduces the influence of
noise by transferring the previous embedding information. Therefore, RNNE can
take into account both static and dynamic characteristics of the network.We
evaluate RNNE on five networks and compare with several state-of-the-art
algorithms. The results demonstrate that RNNE has advantages over other
algorithms in reconstruction, classification and link predictions.
