Degree Fluctuations and the Convergence Time of Consensus Algorithms

TL;DR

This paper analyzes the convergence time of a consensus algorithm on fixed-degree graphs, providing bounds and relating the results to inhomogeneous random walks, with implications for time-varying environments.

Contribution

It establishes a convergence time bound for consensus algorithms with fixed degrees and connects these results to random walk theory, also highlighting the impact of degree fluctuations.

Findings

Consensus achieved within specified accuracy in time proportional to graph parameters.

Convergence time can grow exponentially with the number of nodes under degree variability.

Results extend to inhomogeneous random walks in time-varying networks.

Abstract

We consider a consensus algorithm in which every node in a sequence of undirected, B-connected graphs assigns equal weight to each of its neighbors. Under the assumption that the degree of each node is fixed (except for times when the node has no connections to other nodes), we show that consensus is achieved within a given accuracy $\epsilon$ on n nodes in time $B+4n^3 B \ln(2n/\epsilon)$. Because there is a direct relation between consensus algorithms in time-varying environments and inhomogeneous random walks, our result also translates into a general statement on such random walks. Moreover, we give a simple proof of a result of Cao, Spielman, and Morse that the worst case convergence time becomes exponentially large in the number of nodes $n$ under slight relaxation of the degree constancy assumption.