  Network representation learning (NRL) technique has been successfully adopted
in various data mining and machine learning applications. Random walk based NRL
is one popular paradigm, which uses a set of random walks to capture the
network structural information, and then employs word2vec models to learn the
low-dimensional representations. However, until now there is lack of a
framework, which unifies existing random walk based NRL models and supports to
efficiently learn from large networks. The main obstacle comes from the diverse
random walk models and the inefficient sampling method for the random walk
generation. In this paper, we first introduce a new and efficient edge sampler
based on Metropolis-Hastings sampling technique, and theoretically show the
convergence property of the edge sampler to arbitrary discrete probability
distributions. Then we propose a random walk model abstraction, in which users
can easily define different transition probability by specifying dynamic edge
weights and random walk states. The abstraction is efficiently supported by our
edge sampler, since our sampler can draw samples from unnormalized probability
distribution in constant time complexity. Finally, with the new edge sampler
and random walk model abstraction, we carefully implement a scalable NRL
framework called UniNet. We conduct comprehensive experiments with five random
walk based NRL models over eleven real-world datasets, and the results clearly
demonstrate the efficiency of UniNet over billion-edge networks.
