Distributed Neighbor Selection in Multi-agent Networks

TL;DR

This paper demonstrates that multi-agent consensus can be maintained or improved by selecting a subset of neighbors for interaction, leveraging Laplacian eigenvector properties for distributed implementation.

Contribution

It introduces a neighbor selection rule based on Laplacian eigenvectors that enhances network performance and provides a distributed algorithm for neighbor identification.

Findings

Neighbor selection can improve consensus performance.

A distributed algorithm for neighbor choice is proposed.

Extensions to signed networks are discussed.

Abstract

Achieving consensus via nearest neighbor rules is an important prerequisite for multi-agent networks to accomplish collective tasks. A common assumption in consensus setup is that each agent interacts with all its neighbors. This paper examines whether network functionality and performance can be maintained-and even enhanced-when agents interact only with a subset of their respective (available) neighbors. As shown in the paper, the answer to this inquiry is affirmative. In this direction, we show that by exploring the monotonicity property of the Laplacian eigenvectors, a neighbor selection rule with guaranteed performance enhancements, can be realized for consensus-type networks. For distributed implementation, a quantitative connection between entries of Laplacian eigenvectors and the "relative rate of change" in the state between neighboring agents is further established; this connection facilitates a distributed algorithm for each agent to identify "favorable" neighbors to interact with. Multi-agent networks with and without external influence are examined, as well as extensions to signed networks. This paper underscores the utility of Laplacian eigenvectors in the context of distributed neighbor selection, providing novel insights into distributed data-driven control of multi-agent systems.