  Playing an essential role in data mining, machine learning has a long history
of being applied to networks on multifarious tasks and has played an essential
role in data mining. However, the discrete and sparse natures of networks often
render it difficult to apply machine learning directly to networks. To
circumvent this difficulty, one major school of thought to approach networks
using machine learning is via network embeddings. On the one hand, this network
embeddings have achieved huge success on aggregated network data in recent
years. On the other hand, learning network embeddings on distributively stored
networks still remained understudied: To the best of our knowledge, all
existing algorithms for learning network embeddings have hitherto been
exclusively centralized and thus cannot be applied to these networks. To
accommodate distributively stored networks, in this paper, we proposed a
multi-agent model. Under this model, we developed the multi-agent network
embedding learning algorithm (MANELA) for learning network embeddings. We
demonstrate MANELA's advantages over other existing centralized network
embedding learning algorithms both theoretically and experimentally. Finally,
we further our understanding in MANELA via visualization and exploration of its
relationship to DeepWalk.
