Coordinated and Uncoordinated Optimization of Networks

TL;DR

This paper explores how spatial networks can be optimized globally or locally to balance infrastructure costs and communication efficiency, revealing distinct network structures and power-law link distributions depending on the optimization process and spatial dimension.

Contribution

It introduces a comparative analysis of globally and locally optimized spatial networks, identifying phase transitions and hierarchical structures in different regimes.

Findings

Global optimization yields small worlds, star-like, and hub-centered trees.

Power-law link length distributions emerge in high-cost regimes.

Local optimization shows dimension-dependent phase transitions and hierarchical structures.

Abstract

In this paper we consider spatial networks that realize a balance between an infrastructure cost (the cost of wire needed to connect the network in space) and communication efficiency, measured by average shortest pathlength. A global optimization procedure yields network topologies in which this balance is optimized. These are compared with network topologies generated by a competitive process in which each node strives to optimize its own cost-communication balance. Three phases are observed in globally optimal configurations for different cost-communication trade-offs: (i) regular small worlds, (ii) star-like networks and (iii) trees with a centre of interconnected hubs. In the latter regime, i.e. for very expensive wire, power laws in the link length distributions $P(w)\propto w^{-\alpha}$ are found, which can be explained by a hierarchical organization of the networks. In contrast, in the local optimization process the presence of sharp transitions between different network regimes depends on the dimension of the underlying space. Whereas for $d=\infty$ sharp transitions between fully connected networks, regular small worlds and highly cliquish periphery-core networks are found, for $d=1$ sharp transitions are absent and the power law behaviour in the link length distribution persists over a much wider range of link cost parameters. The measured power law exponents are in agreement with the hypothesis that the locally optimized networks consist of multiple overlapping sub-optimal hierarchical trees.