This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.
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We present DeepWalk, a novel approach for learning latent representations of vertices in a network.
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Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec
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Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.
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