COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |
University of Cambridge > Talks.cam > Probability > The contact process over a dynamical d-regular graph
The contact process over a dynamical d-regular graphAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Perla Sousi. We consider the contact process on a dynamic graph defined as a random d-regular graph with a stationary edge-switching dynamics. In this graph dynamics, independently of the contact process state, each pair {e1,e2} of edges of the graph is replaced by new edges {e′1,e′2} in a crossing fashion: each of e′1,e′2 contains one vertex of e1 and one vertex of e2. As the number of vertices of the graph is taken to infinity, we scale the rate of switching in a way that any fixed edge is involved in a switching with a rate that approaches a limiting value v, so that locally the switching is seen in the same time scale as that of the contact process. We prove that if the infection rate of the contact process is above a threshold value lambda_c (depending on d and v), then the infection survives for a time that grows exponentially with the size of the graph. By proving that lambda_c is strictly smaller than the lower critical infection rate of the contact process on the infinite d-regular tree, we show that there are values of lambda for which the infection dies out in logarithmic time in the static graph but survives exponentially long in the dynamic graph. Joint work with Gabriel Leite Baptista da Silva and Roberto I. Oliveira. This talk is part of the Probability series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsPatient-past based precision medicine: multi-morbidities in a life-course perspective Physics of Living Matter PLM6 MRC Chaucer ClubOther talksThe Anne McLaren Lecture: Embryonic and adult neural stem cells- what underlies their difference Can we use network analysis to predict violence? Grand Rounds - Soft Tissue The ubiquitous acoustic bubble: a brief introduction Observing the Southern Ocean carbon cycle with autonomous floats Root and community inference on Markovian models of networks |