Random Interval Graphs

Nicholas Pippenger*
([email protected])

Department of Computer Science
The University of British Columbia
Vancouver, British Columbia V6T 1Z4
CANADA

Abstract: We consider models for random interval graphs that are based on stochastic service systems, with vertices corresponding to customers and edges corresponding to pairs of customers that are in the system simultaneously. The number N of vertices in a connected component thus corresponds to the number of customers arriving during a busy period, while the size K of the largest clique (which for interval graphs is equal to the chromatic number) corresponds to the maximum number of customers in the system during a busy period. We obtain the following results for both the M=D=1 and the M=M=1 models, with arrival rate >= per mean service time. The expected number of vertices is e>=, and the distribution of the N=e>= tends to an exponential distribution with mean 1 as >= tends to infinity. This implies that log N is very strongly concentrated about >= ? (where is Euler's constant), with variance just ss2=6. The size K of the largest clique is very strongly concentrated about e>=. Thus the ratio K= log N is strongly concentrated about e, in contrast with the situation for random graphs generated by unbiased coin flips, where K= log N is very strongly concentrated about 2= log 2.

* This research was supported by an NSERC Operating Grant.