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Traditional network representation generally uses high-dimensional sparse vectors. However, the high-dimensional sparse representation has become a limitation when people use statistical learning methods, because the high-dimensional vector will spend more time and computing space. With the development of representation learning technology, researchers turn to explore the low dimensional and dense vector representation of nodes in the network. It is a challenge to effectively integrate the network structure and external information of nodes to form a more differentiated network representation.
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