• Corpus ID: 195767446

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

@article{Muscoloni2019AngularSO,
  title={Angular separability of data clusters or network communities in geometrical space and its relevance to hyperbolic embedding},
  author={Alessandro Muscoloni and Carlo Vittorio Cannistraci},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.00025}
}
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. In the hyperbolic space, the radial coordinate of the nodes characterizes their hierarchy, whereas the angular distance… 
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