SIMIS COLLOQUIUM SERIES
Speaker: Prof. Michel Mandjes (Leiden University)
Joint work with Peter Braunsteins (Sydney), Rajat Hazra (Leiden), Frank den Hollander (Leiden), and Jiesen Wang (Amsterdam).
| Date | Time | Venue | Online |
| Nov. 19th | 14:00 | Auditorium, 18F, SIMIS | Zoom Meeting ID: 819 8807 6645 Passcode: SIMIS |
Abstract
The bulk of the random graph literature concerns models that are of an inherently static nature, in that features of the random graph at a single point in time are considered. There are strong practical motivations, however, to consider random graphs that are stochastically evolving, so as to model networks’ inherent dynamics.
In this talk I’ll discuss a set of dynamic random graph mechanisms and their probabilistic properties. Key results cover functional diffusion limits for subgraph counts (describing the behaviour around the mean) and a sample-path large-deviation principle (describing the rare-event behaviour, thus extending the seminal result for the static case developed by Chatterjee and Varadhan).
The last part of my talk will be about estimation of the model parameters from partial information. We for instance demonstrate how the model’s underlying parameters can be estimated from just snapshots of the number of edges. We also consider settings in which particles move around on a dynamically evolving random graph, and in which the graph dynamics are inferred from the movements of the particles (i.e., not observing the graph process).
Speaker Bio
Michel Mandjes is a Professor of Probability and Operations Research at Leiden University. His research spans stochastic processes, queueing, efficient simulation, and probabilistic analysis of networks. He currently serves as Editor-in-Chief of Queueing Systems and has held editorial roles across applied probability and operations research journals.
This is a public lecture to All Researchers, PhD and MS Students at SIMIS Especially to the audience with an interest in the mathematics of network modeling, (random) graph theory, probability and statistics on rare events, or inference under partial observation Everyone is welcome to attend.
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