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EPJ Data Science
Low-dimensional vector representations of network nodes have proven successful to feed graph data to machine learning algorithms and to improve performance across diverse tasks. Most of the embedding techniques, however, have been developed with the goal of achieving dense, low-dimensional encoding of network structure and patterns. Here, we present a node embedding technique aimed at providing low-dimensional feature vectors that are informative of dynamical processes occurring over temporaldoi:10.1140/epjds/s13688-021-00277-8 doaj:98a05955517141bc8d9d413d1a726ce8 fatcat:kig3otolenbkxhzsd7gjpkapnm