Visualization and machine learning analysis of complex networks in hyperspherical space
Visualization and machine learning analysis of complex networks in hyperspherical space is a scholarly work, published in 2019 in ''Pattern Recognition''. The main subjects of the publication include topological data analysis, complex network, visualization, Euclidean space, data mining, artificial intelligence, electronic circuit topology, biological function, theoretical computer science, embedding, gene regulatory network, benchmark, machine learning, cluster analysis, and computer science. The authors use one of the geometric parameters of this embedding, namely the angle between the position vectors of the nodes in the hyperspheres, to extract structural information from networks.