--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. T o 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: T o 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. T o 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. I NTRODUCTION Playing an essential role in data mining, machine learning has a long history of being applied to networks on multifarious tasks, such as network classification [1], prediction of protein binding [2], etc. Thanks to the advancement of technologies such as the Internet and database management systems, the amount of data that are available for machine learning algorithms have been growing tremendously over the past decade. Among these datasets, a huge fraction can be modeled as networks, such as web networks, brain networks, citation networks, street networks, etc. [3]. Therefore, improving machine learning algorithms on networks has become even more important. 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 [4].