January 28 - 2005
introduction | talks | archive | contact | location |
11:15-13:00 | name: Maria Deijfen | title: Stochastic models for spatial growth and competition |
abstract: One of the simplest models for spatial growth and competition is the Richardson model. The original version describes the growth of an infectious phenomenon on Z^d, but the mechanism can also be extended to comprise two phenomena, making it a model for competition on Z^d. In this talk, continuum counterparts of both the one-type and the two-type Richardson model are defined. These models describe growth and competition respectively on R^d instead of Z^d. The main result for the continuum one-type model is a shape theorem where the rotational invariance with R^d allows for a stronger conclusion than in the discrete case. For the two-type continuum model, the question at issue is whether the infection types can grow to occupy infinite parts of R^d simultaneously and it is conjectured that this is possible if and only if the infection types have the same intensity. Existing results are described in the talk along with a number of open problems. |
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14:15-16:00 | name: Gerard Hooghiemstra | title: Distances in random graphs with i.i.d. degrees |
abstract: In this talk I will present results on a random graph with N nodes, where node j has degree D_j and \{D_j\}_{j=1}^N are i.i.d. with Prob(D_j <= x)=F(x). Our main assumption is that 1-F(x)= x^{-\tau+1}L(x) for some \tau>1, and where L is slowly varying at infinity. The graph model is a variant of the so-called configuration model. The minimal number of edges between two arbitrary connected nodes, also known as the graph distance or the hopcount, is investigated when N\rightarrow \infty. We prove that for \tau>3 the graph distance grows like \log_{\nu}N, where the base of the logarithm equals \nu=E[D_j(D_j -1)]/\E[D_j]>1. This confirms the heuristic argument of Newman, Strogatz and Watts. In addition we characterize the asymptotics of the random fluctuations around \log_{\nu}{N}. For \tau \in (2,3) and under some additional technical assumption, we prove that the graph distance grows like 2 \log\log N |\log(\tau-2)|. Again we are able to characterize the asymptotics of the random fluctuations around this mean. Finally for \tau\in (1,2), the graph distance is concentrated on the values 2 and 3, as N\to \infty. For \tau>3, the talk is based on a paper written jointly with Remco van der Hofstad and Piet Van Mieghem. The other cases are based on two papers together with Remco van der Hofstad and Dmitri Znamenski. A survey article, that treats all three regions for \tau can be downloaded from the website: http://ssor.twi.tudelft.nl/~gerardh/ |