  Graph embedding techniques, which learn low-dimensional representations of a
graph, are achieving state-of-the-art performance in many graph mining tasks.
Most existing embedding algorithms assign a single vector to each node,
implicitly assuming that a single representation is enough to capture all
characteristics of the node. However, across many domains, it is common to
observe pervasively overlapping community structure, where most nodes belong to
multiple communities, playing different roles depending on the contexts. Here,
we propose persona2vec, a graph embedding framework that efficiently learns
multiple representations of nodes based on their structural contexts. Using
link prediction-based evaluation, we show that our framework is significantly
faster than the existing state-of-the-art model while achieving better
performance.
