  Embedding large graphs in low dimensional spaces has recently attracted
significant interest due to its wide applications such as graph visualization,
link prediction and node classification. Existing methods focus on computing
the embedding for static graphs. However, many graphs in practical applications
are dynamic and evolve constantly over time. Naively applying existing
embedding algorithms to each snapshot of dynamic graphs independently usually
leads to unsatisfactory performance in terms of stability, flexibility and
efficiency. In this work, we present an efficient algorithm DynGEM based on
recent advances in deep autoencoders for graph embeddings, to address this
problem. The major advantages of DynGEM include: (1) the embedding is stable
over time, (2) it can handle growing dynamic graphs, and (3) it has better
running time than using static embedding methods on each snapshot of a dynamic
graph. We test DynGEM on a variety of tasks including graph visualization,
graph reconstruction, link prediction and anomaly detection (on both synthetic
and real datasets). Experimental results demonstrate the superior stability and
scalability of our approach.
