Graph Representation Learning methods have enabled a wide range of learning
problems to be addressed for data that can be represented in graph form.
Nevertheless, several real world problems in economy, biology, medicine and
other fields raised relevant scaling problems with existing methods and their
software implementation, due to the size of real world graphs characterized by
millions of nodes and billions of edges. We present GraPE, a software resource
for graph processing and random walk based embedding, that can scale with large
and high-degree graphs and significantly speed up-computation. GraPE comprises
specialized data structures, algorithms, and a fast parallel implementation
that displays everal orders of magnitude improvement in empirical space and
time complexity compared to state of the art software resources, with a
corresponding boost in the performance of machine learning methods for edge and
node label prediction and for the unsupervised analysis of graphs.GraPE is
designed to run on laptop and desktop computers, as well as on high performance
computing cluster