  Network representation learning in low dimensional vector space has attracted
considerable attention in both academic and industrial domains. Most real-world
networks are dynamic with addition/deletion of nodes and edges. The existing
graph embedding methods are designed for static networks and they cannot
capture evolving patterns in a large dynamic network. In this paper, we propose
a dynamic embedding method, dynnode2vec, based on the well-known graph
embedding method node2vec. Node2vec is a random walk based embedding method for
static networks. Applying static network embedding in dynamic settings has two
crucial problems: 1) Generating random walks for every time step is time
consuming 2) Embedding vector spaces in each timestamp are different. In order
to tackle these challenges, dynnode2vec uses evolving random walks and
initializes the current graph embedding with previous embedding vectors. We
demonstrate the advantages of the proposed dynamic network embedding by
conducting empirical evaluations on several large dynamic network datasets.
