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BDNE: A Method of Bi-Directional Distance Network Embedding

In order to capture the directed relationship between nodes more accurately, this paper proposed a novel network embedding model called BDNE. The model adds the bi-directional distance while preserving the co-occurrence frequency of the nodes within a context window, which is of great significance in some applications of social networks, such as public opinion monitoring and control, and group discovery. Experimental results show that the embedding results of our model as features in the node classification task are better than DeepWalk, Node2Vector and Line on real data sets of different types and sizes.