We study the continuous-time consensus problem where nodes on a graph attempt to reach average consensus. We consider communication graphs that can be decomposed into a hierarchical structure and present a consensus scheme that exploits this hierarchical topology. The scheme consists of splitting the overall graph into layers of smaller connected subgraphs. Consensus is performed within the ind...

In this article we study graph-distance convergence of monotone operators. First, we prove a property that has been an open problem up to now: the limit of a sequence of graph-distance convergent maximal monotone operators in a Hilbert space is a maximal monotone operator. Next, we show that a sequence of maximal monotone operators converging in the same sense in a reflexive Banach space is uni...

We investigate some well known graph theoretic problems from a game theoretic point of view. To coloring and matching problems we associate binary payoff games where the players are the vertices of the graph. Solutions to the graph problems correspond to action profiles of the game, where all players get payoff 1. We show, that there exist rules for the choice of action in the repeated play of ...

Several stochastic models were proposed recently to model the dynamic evolution of the web graph. We study the infinite limits of the stochastic processes proposed to model the web graph when time goes to infinity. We prove that deterministic variations of the so-called copying model can lead to several nonisomorphic limits. Some models converge to the infinite random graph R, while the converg...

This paper focuses on analyzing the finite-time convergence of a nonlinear consensus algorithm for multi-agent networks with unknown inherent nonlinear dynamics. Due to the existence of the unknown inherent nonlinear dynamics, the stability analysis and the finite-time convergence analysis of the closed-loop system under the proposed consensus algorithm are more challenging than those under the...

Measuring the connectivity strength between a pair of vertices in a graph is one of the most vital concerns in numerous computational graph problems. In this paper we propose a local measure, together with an efficient algorithm that is scalable and parallelizable. In the heart of the algorithm is an iterative process that propagates and refines random values associated with each vertex. The co...

Some spectral properties of the transition matrix of an oriented graph indicate the preconditioning of Euler-Richardson (ER) iterative scheme as a good way to compute efficiently the vertexrank vector associated with such graph. We choose the preconditioner from an algebra U of matrices, thereby obtaining an ERU method, and we observe that ERU can outperform ER in terms of rate of convergence. ...

This paper is about estimating the parameters of the exponential random graph model, also known as the p∗ model, using frequentist Markov chain Monte Carlo (MCMC) methods. The exponential random graph model is simulated using Gibbs or MetropolisHastings sampling. The estimation procedures considered are based on the Robbins-Monro algorithm for approximating a solution to the likelihood equation...

We study the linear distributed asymptotic agreement(consensus) problem for a network of dynamic agents whose communication network is modeled by a randomly switching graph. The switching is determined by a finite state, Markov process, each topology corresponding to a state of the process. We address both the cases where the dynamics of the agents is expressed in continuous and discrete time. ...

We generalize the notion of quasirandom which concerns a class of equivalent properties that random graphs satisfy. We show that the convergence of a graph sequence under the spectral distance is equivalent to the convergence using the (normalized) cut distance. The resulting graph limit is called graphlets. We then consider several families of graphlets and, in particular, we characterize quas...