Binary-state dynamics (such as the susceptible-infected-susceptible (SIS) model of disease spread, or Glauber spin dynamics) on random networks are accurately approximated using master equations. Standard mean-field and pairwise theories are shown to result from seeking approximate solutions of the master equations. Applications to the calculation of SIS epidemic thresholds and critical points of nonequilibrium spin models are also demonstrated.
Hurd, Thomas R; Gleeson, James P.; Melnik, Sergey(Public Library of Science, 2017)
We introduce a probabilistic framework that represents stylized banking networks with the aim of predicting the size of contagion events. Most previous work on random financial networks assumes independent connections ...
Gleeson, James P.; Melnik, Sergey(American Physical Society, 2009)
An analytical approach to calculating bond percolation thresholds, sizes of k-cores, and sizes of giant connected components on structured random networks with nonzero clustering is presented. The networks are generated ...
Onaga, Tomokatsu; Gleeson, James P.; Masuda, Naoki(American Physical Society, 2017)
Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically ...
Gleeson, James P.; Durrett, Rick(Nature Publishing Group, 2017)
An avalanche or cascade occurs when one event causes one or more subsequent events,
which in turn may cause further events in a chain reaction. Avalanching dynamics are studied in many disciplines, with a recent focus on ...
O'Sullivan, David J.P.; Garduño-Hernández, Guillermo; Gleeson, James P.; Beguerisse-Díaz, Mariano(The Royal Society, 2017)
We examine the relationship between social structure and sentiment through the analysis of a large collection of tweets about the Irish Marriage Referendum of 2015. We obtain the
sentiment of every tweet with the hashtags ...
