Traditional network embedding primarily focuses on learning a continuous vector representation for each node, preserving structure and/or node content information, such that off-the-shelf machine algorithms can be easily applied to the vector-format representations analysis. However, learned are inefficient large-scale similarity search, which often involves finding nearest neighbors measured b...