Many successful methods have been proposed for learning low dimensional
representations on large-scale networks, while almost all existing methods are
designed in inseparable processes, learning embeddings for entire networks even
when only a small proportion of nodes are of interest. This leads to great
inconvenience, especially on super-large or dynamic ne