  Network embedding is an important step in many different computations based
on graph data. However, existing approaches are limited to small or middle size
graphs with fewer than a million edges. In practice, web or social network
graphs are orders of magnitude larger, thus making most current methods
impractical for very large graphs. To address this problem, we introduce a new
distributed-memory parallel network embedding method based on Apache Spark and
GraphX. We demonstrate the scalability of our method as well as its ability to
generate meaningful embeddings for vertex classification and link prediction on
both real-world and synthetic graphs.
