Visualization and machine learning analysis of complex networks in hyperspherical space
作者：
Highlights：
• Networks and graphs are naturally embedded in Euclidean hyperspheres.
• Communicability embedding of networks/graphs reveals clusters in networks.
• Nonmetric multidimensional scaling allows visualization of networks in 3D communicability space.
• Communicability clusters of papers in a citation network reveal levels of mathematization.
• Communicability clusters in a gene-gene network reveal genes that co-participate in cancer and other diseases.
摘要
•Networks and graphs are naturally embedded in Euclidean hyperspheres.•Communicability embedding of networks/graphs reveals clusters in networks.•Nonmetric multidimensional scaling allows visualization of networks in 3D communicability space.•Communicability clusters of papers in a citation network reveal levels of mathematization.•Communicability clusters in a gene-gene network reveal genes that co-participate in cancer and other diseases.
论文关键词：Networks,Clustering algorithms,Geometric embedding,Communicability,Matrix functions,Network communities
论文评审过程：Received 7 May 2018, Revised 13 August 2018, Accepted 27 September 2018, Available online 3 October 2018, Version of Record 10 October 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.09.018
