Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling

20 Jul 2019Pedro Almagro-BlancoFernando Sancho-Caparrini

Network embedding techniques inspired by word2vec represent an effective unsupervised relational learning model. Commonly, by means of a Skip-Gram procedure, these techniques learn low dimensional vector representations of the nodes in a graph by sampling node-context examples... (read more)

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