  We propose a neural embedding algorithm called Network Vector, which learns
distributed representations of nodes and the entire networks simultaneously. By
embedding networks in a low-dimensional space, the algorithm allows us to
compare networks in terms of structural similarity and to solve outstanding
predictive problems. Unlike alternative approaches that focus on node level
features, we learn a continuous global vector that captures each node's global
context by maximizing the predictive likelihood of random walk paths in the
network. Our algorithm is scalable to real world graphs with many nodes. We
evaluate our algorithm on datasets from diverse domains, and compare it with
state-of-the-art techniques in node classification, role discovery and concept
analogy tasks. The empirical results show the effectiveness and the efficiency
of our algorithm.
