  A graph embedding algorithm embeds a graph into a low-dimensional space such
that the embedding preserves the inherent properties of the graph. While graph
embedding is fundamentally related to graph visualization, prior work did not
exploit this connection explicitly. We develop Force2Vec that uses
force-directed graph layout models in a graph embedding setting with an aim to
excel in both machine learning (ML) and visualization tasks. We make Force2Vec
highly parallel by mapping its core computations to linear algebra and
utilizing multiple levels of parallelism available in modern processors. The
resultant algorithm is an order of magnitude faster than existing methods (43x
faster than DeepWalk, on average) and can generate embeddings from graphs with
billions of edges in a few hours. In comparison to existing methods, Force2Vec
is better in graph visualization and performs comparably or better in ML tasks
such as link prediction, node classification, and clustering. Source code is
available at https://github.com/HipGraph/Force2Vec.
