arXiv subjects

Colin Cooper

Publications and source records attributed to Colin Cooper.

48 records · Page 3Linked to original sources

arXiv2014

The height of random $k$-trees and related branching processes

We consider the height of random k-trees and k-Apollonian networks. These random graphs are not really trees, but instead have a tree-like structure. The height will be the maximum distance of a vertex from the root. We show that w.h.p. the height of random k-trees and k-Apollonian networks is asymptotic to clog t, where t is the number of vertices, and c=c(k) is given as the solution to a transcendental equation. The equations are slightly different for the two types of process. In the limit as k-->oo the height of both processes is asymptotic to log t/(k log 2).

math.CO↗

arXiv2014

Cover time of a random graph with a degree sequence II: Allowing vertices of degree two

We study the cover time of a random graph chosen uniformly at random from the set of graphs with vertex set $[n]$ and degree sequence $\mathbf{d}=(d_i)_{i=1}^n$. In a previous work, the asymptotic cover time was obtained under a number of assumptions on $\mathbf{d}$, the most significant being that $d_i\geq 3$ for all $i$. Here we replace this assumption by $d_i\geq 2$. As a corollary, we establish the asymptotic cover time for the 2-core of the emerging giant component of $\mathcal{G}(n,p)$.

math.CO↗

arXiv2014

The Power of Two Choices in Distributed Voting

Distributed voting is a fundamental topic in distributed computing. In pull voting, in each step every vertex chooses a neighbour uniformly at random, and adopts its opinion. The voting is completed when all vertices hold the same opinion. On many graph classes including regular graphs, pull voting requires $Θ(n)$ expected steps to complete, even if initially there are only two distinct opinions. In this paper we consider a related process which we call two-sample voting: every vertex chooses two random neighbours in each step. If the opinions of these neighbours coincide, then the vertex revises its opinion according to the chosen sample. Otherwise, it keeps its own opinion. We consider the performance of this process in the case where two different opinions reside on vertices of some (arbitrary) sets $A$ and $B$, respectively. Here, $|A| + |B| = n$ is the number of vertices of the graph. We show that there is a constant $K$ such that if the initial imbalance between the two opinions is ?$ν_0 = (|A| - |B|)/n \geq K \sqrt{(1/d) + (d/n)}$, then with high probability two sample voting completes in a random $d$ regular graph in $O(\log n)$ steps and the initial majority opinion wins. We also show the same performance for any regular graph, if $ν_0 \geq K λ_2$ where $λ_2$ is the second largest eigenvalue of the transition matrix. In the graphs we consider, standard pull voting requires $Ω(n)$ steps, and the minority can still win with probability $|B|/n$.

cs.DS↗

arXiv2014

Long paths in random Apollonian networks

We consider the length $L(n)$ of the longest path in a randomly generated Apollonian Network (ApN) ${\cal A}_n$. We show that w.h.p. $L(n)\leq ne^{-\log^cn}$ for any constant $c<2/3$.

math.PR↗

arXiv2013

On the length of a random minimum spanning tree

We study the expected value of the length $L_n$ of the minimum spanning tree of the complete graph $K_n$ when each edge $e$ is given an independent uniform $[0,1]$ edge weight. We sharpen the result of Frieze \cite{F1} that $\lim_{n\to\infty}\E(L_n)=\z(3)$ and show that $\E(L_n)=\z(3)+\frac{c_1}{n}+\frac{c_2+o(1)}{n^{4/3}}$ where $c_1,c_2$ are explicitly defined constants.

math.CO↗

arXiv2012

Random walks which prefer unvisited edges. Exploring high girth even degree expanders in linear time

We consider a modified random walk which uses unvisited edges whenever possible, and makes a simple random walk otherwise. We call such a walk an edge-process. We assume there is a rule A, which tells the walk which unvisited edge to use whenever there is a choice. In the simplest case, A is a uniform random choice over unvisited edges incident with the current walk position. However we do not exclude arbitrary choices of rule A. For example, the rule could be determined on-line by an adversary, or could vary from vertex to vertex. For even degree expander graphs, of bounded maximum degree, we have the following result. Let G be an n vertex even degree expander graph, for which every vertex is in at least one vertex induced cycle of length L. Any edge-process on G has cover time (n+ (n log n)/L). This result is independent of the rule A used to select the order of the unvisited edges, which can be chosen on-line by an adversary. As an example, With high probability, random r-regular graphs, (r at least 4, even), are expanders for which L = Omega(log n). Thus, for almost all such graphs, the vertex cover time of the edge-process is Theta(n). This improves the vertex cover time of such graphs by a factor of log n, compared to the Omega(n log n) cover time of any weighted random walk.

cs.DS↗

arXiv2012

Corrigendum: Sampling regular graphs and a peer-to-peer network

In [Combinatorics, Probability and Computing 16 (2007), 557 - 593, Theorem 1] we proved a polynomial-time bound on the mixing rate of the switch chain for sampling d-regular graphs. This corrigendum corrects a technical error in the proof. In order to fix the error, we must multiply the bound on the mixing time by a factor of d^8 .

math.CO↗

arXiv2011

Component structure of the vacant set induced by a random walk on a random graph

We consider random walks on several classes of graphs and explore the likely structure of the vacant set, i.e. the set of unvisited vertices. Let Γ(t) be the subgraph induced by the vacant set of the walk at step t. We show that for random graphs G_{n,p} (above the connectivity threshold) and for random regular graphs G_r, r \geq 3, the graph Γ(t) undergoes a phase transition in the sense of the well-known Erdos-Renyi phase transition. Thus for t \leq (1-ε)t^*, there is a unique giant component, plus components of size O(log n), and for t \geq (1+ε)t^* all components are of size O(log n). For G_{n,p} and G_r we give the value of t^*, and the size of Γ(t). For G_r, we also give the degree sequence of Γ(t), the size of the giant component (if any) of Γ(t) and the number of tree components of Γ(t) of a given size k=O(log n). We also show that for random digraphs D_{n,p} above the strong connectivity threshold, there is a similar directed phase transition. Thus for t\leq (1-ε)t^*, there is a unique strongly connected giant component, plus strongly connected components of size O(log n), and for t\geq (1+ε)t^* all strongly connected components are of size O(log n).

math.CO↗

arXiv2011

Stationary distribution and cover time of random walks on random digraphs

We study properties of a simple random walk on the random digraph D_{n,p} when np={d\log n},\; d>1. We prove that whp the stationary probability pi_v of a vertex v is asymptotic to deg^-(v)/m where deg^-(v) is the in-degree of v and m=n(n-1)p is the expected number of edges of D_{n,p}. If d=d(n) tends to infinity with n, the stationary distribution is asymptotically uniform whp. Using this result we prove that, for d>1, whp the cover time of D_{n,p} is asymptotic to d\log (d/(d-1))n\log n. If d=d(n) tends to infinity with n, then the cover time is asymptotic to n\log n.

math.CO↗

arXiv2011

On the Imitation Strategy for Games on Graphs

In evolutionary game theory, repeated two-player games are used to study strategy evolution in a population under natural selection. As the evolution greatly depends on the interaction structure, there has been growing interests in studying the games on graphs. In this setting, players occupy the vertices of a graph and play the game only with their immediate neighbours. Various evolutionary dynamics have been studied in this setting for different games. Due to the complexity of the analysis, however, most of the work in this area is experimental. This paper aims to contribute to a more complete understanding, by providing rigorous analysis. We study the imitation dynamics on two classes of graph: cycles and complete graphs. We focus on three well known social dilemmas, namely the Prisoner's Dilemma, the Stag Hunt and the Snowdrift Game. We also consider, for completeness, the so-called Harmony Game. Our analysis shows that, on the cycle, all four games converge fast, either to total cooperation or total defection. On the complete graph, all but the Snowdrift game converge fast, either to cooperation or defection. The Snowdrift game reaches a metastable state fast, where cooperators and defectors coexist. It will converge to cooperation or defection only after spending time in this state which is exponential in the size, n, of the graph. In exceptional cases, it will remain in this state indefinitely. Our theoretical results are supported by experimental investigations.

cs.GT↗

arXiv2010

Chains-into-Bins Processes

The study of {\em balls-into-bins processes} or {\em occupancy problems} has a long history. These processes can be used to translate realistic problems into mathematical ones in a natural way. In general, the goal of a balls-into-bins process is to allocate a set of independent objects (tasks, jobs, balls) to a set of resources (servers, bins, urns) and, thereby, to minimize the maximum load. In this paper, we analyze the maximum load for the {\em chains-into-bins} problem, which is defined as follows. There are $n$ bins, and $m$ objects to be allocated. Each object consists of balls connected into a chain of length $\ell$, so that there are $m \ell$ balls in total. We assume the chains cannot be broken, and that the balls in one chain have to be allocated to $\ell$ consecutive bins. We allow each chain $d$ independent and uniformly random bin choices for its starting position. The chain is allocated using the rule that the maximum load of any bin receiving a ball of that chain is minimized. We show that, for $d \ge 2$ and $m\cdot\ell=O(n)$, the maximum load is $((\ln \ln m)/\ln d) +O(1)$ with probability $1-\tilde O(1/m^{d-1})$.

cs.DS↗

arXiv2006

Energy Efficient Randomized Communication in Unknown AdHoc Networks

This paper studies broadcasting and gossiping algorithms in random and general AdHoc networks. Our goal is not only to minimise the broadcasting and gossiping time, but also to minimise the energy consumption, which is measured in terms of the total number of messages (or transmissions) sent. We assume that the nodes of the network do not know the network, and that they can only send with a fixed power, meaning they can not adjust the areas sizes that their messages cover. We believe that under these circumstances the number of transmissions is a very good measure for the overall energy consumption. For random networks, we present a broadcasting algorithm where every node transmits at most once. We show that our algorithm broadcasts in $O(\log n)$ steps, w.h.p, where $n$ is the number of nodes. We then present a $O(d \log n)$ ($d$ is the expected degree) gossiping algorithm using $O(\log n)$ messages per node. For general networks with known diameter $D$, we present a randomised broadcasting algorithm with optimal broadcasting time $O(D \log (n/D) + \log^2 n)$ that uses an expected number of $O(\log^2 n / \log (n/D))$ transmissions per node. We also show a tradeoff result between the broadcasting time and the number of transmissions: we construct a network such that any oblivious algorithmusing a time-invariant distribution requires $Ω(\log^2 n / \log (n/D))$ messages per node in order to finish broadcasting in optimal time. This demonstrates the tightness of our upper bound. We also show that no oblivious algorithm can complete broadcasting w.h.p. using $o(\log n)$ messages per node.

cs.DC↗