Communication Network Design: Balancing Modularity and Mixing via Optimal Graph Spectra

TL;DR

This paper introduces a graph spectral approach to designing communication networks that balance modularity and mixing time, optimizing organizational structures for improved performance.

Contribution

It develops a novel framework linking social network features to graph spectra and uses non-convex programming to optimize network design for specific structural goals.

Findings

Liaisons outperform brokers in balancing modularity and mixing.

Spectral graph methods effectively encode organizational structure objectives.

Optimized networks enhance communication efficiency and modularity.

Abstract

By leveraging information technologies, organizations now have the ability to design their communication networks and crowdsourcing platforms to pursue various performance goals, but existing research on network design does not account for the specific features of social networks, such as the notion of teams. We fill this gap by demonstrating how desirable aspects of organizational structure can be mapped parsimoniously onto the spectrum of the graph Laplacian allowing the specification of structural objectives and build on recent advances in non-convex programming to optimize them. This design framework is general, but we focus here on the problem of creating graphs that balance high modularity and low mixing time, and show how "liaisons" rather than brokers maximize this objective.