Prof. Emo Welzl and Prof. Bernd Gärtner
|Mittagsseminar Talk Information|
Date and Time: Tuesday, November 26, 2013, 12:15 pm
Duration: 30 minutes
Location: CAB G51
Speaker: Hafsteinn Einarsson
Bootstrap Percolation is a process which simulates spread of activity in a graph. The process starts with some vertices being active (bootstrap) and follows a rule to determine which vertices will as a result turn active.
This process has been studied on various graph classes, with various rules to determine how a vertex turns active. We build on a result by Janson, Łuczak, Turova and Vallier. They proved that for G(n,p), if a vertices are initially active and if a vertex turns active if it has at least k active neighbors for some constant k, then the process will either activate all vertices or fewer than 2a vertices a.a.s.
This all-or-nothing behavior is prevalent in the study of bootstrap percolation. Inspired by biological neural networks we decided to study if this activity explosion could be controlled. First we extended the process from above by introducing inhibitory vertices and changing the rule such that a vertex turns active if it has k more excitatory than inhibitory neighbors. Second, we further extend the process by introducing a random delay on every edge in the graph.
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