We study the Undecided-State Dynamics (USD), a fundamental consensus process in which each vertex holds one of k decided opinions or the undecided state. We consider both the gossip model and the population protocol model. Prior work established tight bounds on the consensus time of this process only for the regime k=O(n/(log⁡n)2) (for the population protocol model) and k = O((n/log n)1/3) (for the gossip model), often under restrictive assumptions on the initial configuration.

We present the first nearly-optimal bounds on the consensus time for the well-known synchronous consensus dynamics, specifically 3-Majority and 2-Choices, for an arbitrary number of opinions. In synchronous consensus dynamics, we consider an n-vertex complete graph with self-loops, where each vertex holds an opinion from {1,..., k}. At each discrete-time round, all vertices update their opinions simultaneously according to a given protocol. The goal is to reach a consensus, where all vertices support the same opinion. In 3-Majority, each vertex chooses three random neighbors with replacement and updates its opinion to match the majority, with ties broken randomly. In 2-Choices, each vertex chooses two random neighbors with replacement. If the selected vertices hold the same opinion, the vertex adopts that opinion. Otherwise, it retains its current opinion for that round.

We consider 3-Majority, a probabilistic consensus dynamics on a complete graph with n vertices, each vertex starting with one of k initial opinions. At each discrete time step, a vertex u is chosen uniformly at random. The selected vertex u chooses three neighbors v1, v2, v3 uniformly at random with replacement and takes the majority opinion held by the three, where ties are broken in favor of the opinion of v3. The main quantity of interest is the consensus time, the number of steps required for all vertices to hold the same opinion. This asynchronous version turns out to be considerably harder to analyze than the synchronous version and so far results have only been obtained for k = 2. Even in the synchronous version the results for large k are far from tight. In this paper we prove that the consensus time is for all k. These are the first bounds for all k that are tight up to a polylogarithmic factor.

We consider a type of pull voting suitable for discrete numeric opinions which can be compared on a linear scale, for example, 1 ("disagree strongly"), 2 ("disagree"), …, 5 ("agree strongly"). On observing the opinion of a random neighbour, a vertex changes its opinion incrementally towards the value of the neighbour’s opinion, if different. For opinions drawn from a set {1,2,…,k}, the opinion of the vertex would change by +1 if the opinion of the neighbour is larger, or by -1, if it is smaller. It is not clear how to predict the outcome of this process, but we observe that the total weight of the system, that is, the sum of the individual opinions of all vertices, is a martingale. This allows us analyse the outcome of the process on some classes of dense expanders such as complete graphs K_n and random graphs G_{n,p} for suitably large p. If the average of the original opinions satisfies i ≤ c ≤ i+1 for some integer i, then the asymptotic probability that opinion i wins is i+1-c, and the probability that opinion i+1 wins is c-i. With high probability, the winning opinion cannot be other than i or i+1. To contrast this, we show that for a path and opinions 0,1,2 arranged initially in non-decreasing order along the path, the outcome is very different. Any of the opinions can win with constant probability, provided that each of the two extreme opinions 0 and 2 is initially supported by a constant fraction of vertices.

We consider a type of pull voting suitable for discrete numeric opinions which can be compared on a linear scale, for example, 1 ('disagree strongly'), 2 ('disagree'), ..., 5 ('agree strongly'). On observing the opinion of a random neighbour, a vertex changes its opinion incrementally towards the value of the neighbour's opinion, if different. For opinions drawn from a set {1, 2, ..., k}, the opinion of the vertex would change by +1 if the opinion of the neighbour is larger, or by −1, if it is smaller.

Real networks are often dynamic. In response to it, analyses of algorithms on dynamic networks attract more and more attention in network science and engineering. Random walks on dynamic graphs also have been investigated actively in more than a decade, where in most cases the edge set changes but the vertex set is static. The vertex sets are also dynamic in many real networks. Motivated by a new technology of the analysis of random walks on dynamic graphs, this paper introduces a simple model of graphs with an increasing number of vertices and presents an analysis of random walks associated with the cover time on such graphs. In particular, we reveal that a random walk asymptotically covers the vertices all but a constant number if the vertex set grows moderately.

Consider a distributed graph where each vertex holds one of two distinct opinions. In this paper, we are interested in synchronous voting processes where each vertex updates its opinion according to a predefined common local updating rule. For example, each vertex adopts the majority opinion among 1) itself and two randomly picked neighbors in best-of-two or 2) three randomly picked neighbors in best-of-three. Previous works intensively studied specific rules including best-of-two and best-of-three individually. In this paper, we generalize and extend previous works of best-of-two and best-of-three on expander graphs by proposing a new model, quasi-majority functional voting. This new model contains best-of-two and best-of-three as special cases. We show that, on expander graphs with sufficiently large initial bias, any quasi-majority functional voting reaches consensus within $O(\log n)$ steps with high probability. Moreover, we show that, for any initial opinion configuration, any quasi-majority functional voting on expander graphs with higher expansion (e.g., Erdős-Rényi graph $G(n,p)$ with $p=Ω(1/\sqrt{n})$) reaches consensus within $O(\log n)$ with high probability. Furthermore, we show that the consensus time is $O(\log n/\log k)$ of best-of-$(2k+1)$ for $k=o(n/\log n)$.

This paper is concerned with voting processes on graphs where each vertex holds one of two different opinions. In particular, we study the \emph{Best-of-two} and the \emph{Best-of-three}. Here at each synchronous and discrete time step, each vertex updates its opinion to match the majority among the opinions of two random neighbors and itself (the Best-of-two) or the opinions of three random neighbors (the Best-of-three). Previous studies have explored these processes on complete graphs and expander graphs, but we understand significantly less about their properties on graphs with more complicated structures. In this paper, we study the Best-of-two and the Best-of-three on the stochastic block model $G(2n,p,q)$, which is a random graph consisting of two distinct Erdős-Rényi graphs $G(n,p)$ joined by random edges with density $q\leq p$. We obtain two main results. First, if $p=ω(\log n/n)$ and $r=q/p$ is a constant, we show that there is a phase transition in $r$ with threshold $r^*$ (specifically, $r^*=\sqrt{5}-2$ for the Best-of-two, and $r^*=1/7$ for the Best-of-three). If $r>r^*$, the process reaches consensus within $O(\log \log n+\log n/\log (np))$ steps for any initial opinion configuration with a bias of $Ω(n)$. By contrast, if $rr^*$, we show that, for any initial opinion configuration, the process reaches consensus within $O(\log n)$ steps. To the best of our knowledge, this is the first result concerning multiple-choice voting for arbitrary initial opinion configurations on non-complete graphs.

The rotor-router model is a deterministic process analogous to a simple random walk on a graph, and the discrepancy of token configurations between the rotor-router model and its corresponding random walk has been investigated in some contexts. Motivated by general Markov chains beyond simple random walks, this paper investigates a generalized model which imitates a Markov chain (of multiple tokens) possibly containing irrational transition probabilities. We are concerned with the vertexwise discrepancy of the numbers of tokens between the generalized model and its corresponding Markov chain, and present an upper bound of the discrepancy in terms of the mixing time of the Markov chain.

In a voting process on a graph vertices revise their opinions in a distributed way based on the opinions of nearby vertices. The voting completes when the vertices reach consensus, that is, they all have the same opinion. The classic example is synchronous pull voting where at each step, each vertex adopts the opinion of a random neighbour. This very simple process, however, can be slow and the final opinion is not necessarily the one with the initial largest support. It was shown earlier that if there are initially only two opposing opinions, then both these drawbacks can be overcome by a synchronous two-sample voting, in which at each step each vertex considers its own opinion and the opinions of two random neighbours. If there are initially three or more opinions, a problem arises when there is no clear majority. One class of opinions may be largest (the plurality opinion), although its total size is less than that of two other opinions put together. We analyse the performance of the two-sample voting on d-regular graphs for this case. We show that, if the difference between the initial sizes A_1 and A_2 of the largest and second largest opinions is at least C n max{sqrt((log n)/A_1), lambda}, then the largest opinion wins in O((n log n)/A_1) steps with high probability. Here C is a suitable constant and lambda is the absolute second eigenvalue of transition matrix P=Adj(G)/d of a simple random walk on the graph G. Our bound generalizes the results of Becchetti et al. [SPAA 2014] for the related three-sample voting process on complete graphs. Our bound implies that if lambda = o(1), then the two-sample voting can consistently converge to the largest opinion, even if A_1 - A_2 = o(n). If lambda is constant, we show that the case A_1 - A_2 = o(n) can be dealt with by sampling using short random walks. Finally, we give a simple and efficient push voting algorithm for the case when there are a number of large opinions and any of them is acceptable as the final winning opinion.