Multiple Random Walks and Interacting Particle Systems

  • Colin Cooper
  • Alan Frieze
  • Tomasz Radzik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5556)

Abstract

We study properties of multiple random walks on a graph under various assumptions of interaction between the particles. To give precise results, we make our analysis for random regular graphs. The cover time of a random walk on a random r-regular graph was studied in [6], where it was shown with high probability (whp), that for r ≥ 3 the cover time is asymptotic to θrn ln n, where θr = (r − 1)/(r − 2). In this paper we prove the following (whp) results. For k independent walks on a random regular graph G, the cover time CG(k) is asymptotic to CG/k, where CG is the cover time of a single walk. For most starting positions, the expected number of steps before any of the walks meet is \(\theta_r n/\binom{k}{2}\). If the walks can communicate when meeting at a vertex, we show that, for most starting positions, the expected time for k walks to broadcast a single piece of information to each other is asymptotic to 2θrn (ln k)/k, as k,n → ∞.

We also establish properties of walks where there are two types of particles, predator and prey, or where particles interact when they meet at a vertex by coalescing, or by annihilating each other. For example, the expected extinction time of k explosive particles (k even) tends to (2ln 2) θrn as k→ ∞.

The case of n coalescing particles, where one particle is initially located at each vertex, corresponds to a voter model defined as follows: Initially each vertex has a distinct opinion, and at each step each vertex changes its opinion to that of a random neighbour. The expected time for a unique opinion to emerge is the expected time for all the particles to coalesce, which is asymptotic to 2 θrn.

Combining results from the predator-prey and multiple random walk models allows us to compare expected detection time in the following cops and robbers scenarios: both the predator and the prey move randomly, the prey moves randomly and the predators stay fixed, the predators move randomly and the prey stays fixed. In all cases, with k predators and ℓ prey the expected detection time is θrHn/k, where H is the ℓ-th harmonic number.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Colin Cooper
    • 1
  • Alan Frieze
    • 2
  • Tomasz Radzik
    • 1
  1. 1.Department of Computer ScienceKing’s College LondonLondonUK
  2. 2.Department of Mathematical SciencesCarnegie Mellon UniversityPittsburghUSA

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