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word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data
[article]
2020
arXiv
pre-print
Vector representations of graphs and relational structures, whether hand-crafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view. Starting with a survey of embedding
arXiv:2003.12590v1
fatcat:ak4ue5wuqzdzbm3bu5tfziy6wy