  Many real world networks are very large and constantly change over time.
These dynamic networks exist in various domains such as social networks,
traffic networks and biological interactions. To handle large dynamic networks
in downstream applications such as link prediction and anomaly detection, it is
essential for such networks to be transferred into a low dimensional space.
Recently, network embedding, a technique that converts a large graph into a
low-dimensional representation, has become increasingly popular due to its
strength in preserving the structure of a network. Efficient dynamic network
embedding, however, has not yet been fully explored. In this paper, we present
a dynamic network embedding method that integrates the history of nodes over
time into the current state of nodes. The key contribution of our work is 1)
generating dynamic network embedding by combining both dynamic and static node
information 2) tracking history of neighbors of nodes using LSTM 3)
significantly decreasing the time and memory by training an autoencoder LSTM
model using temporal walks rather than adjacency matrices of graphs which are
the common practice. We evaluate our method in multiple applications such as
anomaly detection, link prediction and node classification in datasets from
various domains.
