Angular separability of data clusters or network communities in geometrical space and its relevance to hyperbolic embedding

28 Jun 2019Alessandro MuscoloniCarlo Vittorio Cannistraci

Analysis of 'big data' characterized by high-dimensionality such as word vectors and complex networks requires often their representation in a geometrical space by embedding. Recent developments in machine learning and network geometry have pointed out the hyperbolic space as a useful framework for the representation of this data derived by real complex physical systems... (read more)

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