Gerard Hooghiemstra

On the efficiency of multicast

The average number of joint hops in a shortest-path multicast tree from a root to m arbitrary chosen group member nodes is studied. A general theory for all graphs, hence including the graph representation of the Internet, is presented which quantifies the multicast reduction in network links compared to m times unicast. For two special types of graphs, the random graph Gp(N) and the k-ary tree, exact and asymptotic results are derived. Comparing these explicit results with previously published Internet measurements [13] indicates that the number of routers in the Internet that can be reached from a root grows exponentially in the number of hops with an effective degree of approximately 3.2.

Power series for stationary distributions of coupled processor models

For the coupled processor model with exponential service times, an approach is presented to calculate the stationary distribution of the queue length. In this approach the stationary probabilities are expressed as power series in the parameter

FIRST-PASSAGE PERCOLATION ON THE RANDOM GRAPH

\rho

First passage percolation on random graphs with finite mean degrees.

, the traffic intensity of the system. The method is not restricted to state spaces (of the underlying continuous time Markov chain) of dimension two, but applies equally well to higher-dimensional state spaces.

Diameters in Preferential Attachment Models

We study first-passage percolation on the random graph Gp(N) with exponentially distributed weights on the links. For the special case of the complete graph, this problem can be described in terms of a continuous-time Markov chain and recursive trees. The Markov chain X(t) describes the number of nodes that can be reached from the initial node in time t. The recursive trees, which are uniform trees of N nodes, describe the structure of the cluster once it contains all the nodes of the complete graph. From these results, the distribution of the number of hops (links) of the shortest path between two arbitrary nodes is derived.We generalize this result to an asymptotic result, as N → ∞, for the case of the random graph where each link is present independently with a probability pN as long as NpN/(log N)3 → ∞. The interesting point of this generalization is that (1) the limiting distribution is insensitive to p and (2) the distribution of the number of hops of the shortest path between two arbitrary nodes has a remarkable fit with shortest path data measured in the Internet.

Asymptotic behavior of random discrete event systems

We study first passage percolation on the configuration model. Assuming that each edge has an independent exponentially distributed edge weight, we derive explicit distributional asymptotics for the minimum weight between two randomly chosen connected vertices in the network, as well as for the number of edges on the least weight path, the so-called hopcount. We analyze the configuration model with degree power-law exponent τ > 2, in which the degrees are assumed to be i.i.d. with a tail distribution which is either of power-law form with exponent τ − 1 > 1, or has even thinner tails (τ = ∞). In this model, the degrees have a finite first moment, while the variance is finite for τ > 3, but infinite for τ ∈ (2, 3). We prove a central limit theorem for the hopcount, with asymptotically equal means and variances equal to α log n, where α ∈ (0, 1) for τ ∈ (2, 3), while α > 1 for τ > 3. Here n denotes the size of the graph. For τ ∈ (2, 3), it is known that the graph distance between two randomly chosen connected vertices is proportional to log log n [25], i.e., distances are ultra small. Thus, the addition of edge weights causes a marked change in the geometry of the network. We further study the weight of the least weight path, and prove convergence in distribution of an appropriately centered version. This study continues the program initiated in [5] of showing that log n is the correct scaling for the hopcount under i.i.d. edge disorder, even if the graph distance between two randomly chosen vertices is of much smaller order. The case of infinite mean degrees (τ ∈ [1, 2)) is studied in [6], where it is proved that the hopcount remains uniformly bounded and converges in distribution.

A preferential attachment model with random initial degrees

In this paper, we investigate the diameter in preferential attachment (PA-) models, thus quantifying the statement that these models are small worlds. The models studied here are such that edges are attached to older vertices proportional to the degree plus a constant, i.e., we consider affine PA-models. There is a substantial amount of literature proving that, quite generally, PA-graphs possess power-law degree sequences with a power-law exponent τ>2.We prove that the diameter of the PA-model is bounded above by a constant times log t, where t is the size of the graph. When the power-law exponent τ exceeds 3, then we prove that log t is the right order, by proving a lower bound of this order, both for the diameter as well as for the typical distance. This shows that, for τ>3, distances are of the order log t. For τ∈(2,3), we improve the upper bound to a constant times log log t, and prove a lower bound of the same order for the diameter. Unfortunately, this proof does not extend to typical distances. These results do show that the diameter is of order log log t.These bounds partially prove predictions by physicists that the typical distance in PA-graphs are similar to the ones in other scale-free random graphs, such as the configuration model and various inhomogeneous random graph models, where typical distances have been shown to be of order log log t when τ∈(2,3), and of order log t when τ>3.

On the covariance of the level sizes in random recursive trees

In this paper we discuss some aspects of the asymptotic behavior of Discrete Event Dynamic Systems (DEDS), in which the activity times are random variables. The main result is that a central limit theorem holds for DEDS and consequently that the cycletime of the system is asymptotically normally distributed.

Scale-free percolation

In this paper, a random graph process {G(t)}t≥1 is studied and its degree sequence is analyzed. Let {Wt}t≥1 be an i.i.d. sequence. The graph process is defined so that, at each integer time t, a new vertex with Wt edges attached to it, is added to the graph. The new edges added at time t are then preferentially connected to older vertices, i.e., conditionally on G(t-1), the probability that a given edge of vertex t is connected to vertex i is proportional to di(t-1)+δ, where di(t-1) is the degree of vertex i at time t-1, independently of the other edges. The main result is that the asymptotical degree sequence for this process is a power law with exponent τ=min{τW,τP}, where τW is the power-law exponent of the initial degrees {Wt}t≥1 and τP the exponent predicted by pure preferential attachment. This result extends previous work by Cooper and Frieze.

Universality for first passage percolation on sparse random graphs

In this paper we study the covariance structure of the number of nodes k and l steps away from the root in random recursive trees. We give an analytic expression valid for all k, l and tree sizes N. The fraction of nodes k steps away from the root is a random probability distribution in k. The expression for the covariances allows us to show that the total variation distance between this (random) probability distribution and its mean converges in probability to zero.