UniNet: Scalable Network Representation Learning with Metropolis-Hastings Sampling

Network representation learning (NRL) 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 exist...