Abstract



. A complex network is a condensed representation of the relational topological framework of a complex system. A main reason for the existence of such networks is the transmission of items through the entities of these complex systems. Here, we consider a communicability function that accounts for the routes through which items flow on networks. Such a function induces a natural embedding of a network in a Euclidean highdimensional sphere. We 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. Such information is extracted by using machine learning techniques, such as nonmetric multidimensional scaling and Kmeans clustering algorithms. The first allows us to reduce the dimensionality of the communicability hyperspheres to 3dimensional ones that allow network visualization. The second permits to cluster the nodes of the networks based on their similarities in terms of their capacity to successfully deliver information through the network. After testing these approaches in benchmark networks and compare them with the most used clustering methods in networks we analyze two realworld examples. In the first, consisting of a citation network, we discover citation groups that reflect the level of mathematics used in their publications. In the second, we discover groups of genes that coparticipate in human diseases, reporting a few genes that coparticipate in cancer and other diseases. Both examples emphasize the potential of the current methodology for the discovery of new patterns in relational data.  
International

Si 
JCR

Si 
Title

Pattern Recognition 
ISBN

00313203 
Impact factor JCR

3,962 
Impact info

Datos JCR del año 2017 
Volume

86 

10.1016/j.patcog.2018.09.018 
Journal number


From page

320 
To page

331 
Month

SIN MES 
Ranking
