Inferring global network properties from egocentric data with applications to epidemics
Last modified: 2014-06-09
Abstract
Contributor: Pieter Trapman
Social networks are often only partly observed. It may be desirable to induce global properties of the network from ''egocentric'' data. In the talk we study different types of egocentric data, and show what global network properties are consistent with data. Two global network properties are considered: the size of the largest connected component (the giant), and the size of an epidemic outbreak taking place on the network. The main conclusion is that in most cases, egocentric data allow for a large range of possible sizes of the giant and the outbreak. The asymptotic size of the giant and the outbreak is also characterised assuming the network is selected uniformly among networks with prescribed egocentric data.
Social networks are often only partly observed. It may be desirable to induce global properties of the network from ''egocentric'' data. In the talk we study different types of egocentric data, and show what global network properties are consistent with data. Two global network properties are considered: the size of the largest connected component (the giant), and the size of an epidemic outbreak taking place on the network. The main conclusion is that in most cases, egocentric data allow for a large range of possible sizes of the giant and the outbreak. The asymptotic size of the giant and the outbreak is also characterised assuming the network is selected uniformly among networks with prescribed egocentric data.