The transsortative structure of networks.

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Bibliographic Details
Title: The transsortative structure of networks.
Authors: Ngo, Shin-Chieng1,2 (AUTHOR) xinzengw@usc.edu, Percus, Allon G.2,3 (AUTHOR), Burghardt, Keith2 (AUTHOR) keithab@isi.edu, Lerman, Kristina2 (AUTHOR)
Source: Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences. May2020, Vol. 476 Issue 2237, p1-16. 16p.
Subjects: Illusion (Philosophy), Neighbors, Sensory perception, Topology, Statistics
Abstract: Network topologies can be highly non-trivial, due to the complex underlying behaviours that form them. While past research has shown that some processes on networks may be characterized by local statistics describing nodes and their neighbours, such as degree assortativity, these quantities fail to capture important sources of variation in network structure. We define a property called transsortativity that describes correlations among a node's neighbours. Transsortativity can be systematically varied, independently of the network's degree distribution and assortativity. Moreover, it can significantly impact the spread of contagions as well as the perceptions of neighbours, known as the majority illusion. Our work improves our ability to create and analyse more realistic models of complex networks. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Network topologies can be highly non-trivial, due to the complex underlying behaviours that form them. While past research has shown that some processes on networks may be characterized by local statistics describing nodes and their neighbours, such as degree assortativity, these quantities fail to capture important sources of variation in network structure. We define a property called transsortativity that describes correlations among a node's neighbours. Transsortativity can be systematically varied, independently of the network's degree distribution and assortativity. Moreover, it can significantly impact the spread of contagions as well as the perceptions of neighbours, known as the majority illusion. Our work improves our ability to create and analyse more realistic models of complex networks. [ABSTRACT FROM AUTHOR]
ISSN:13645021
DOI:10.1098/rspa.2019.0772