Enhanced Flow in Small-World Networks

TL;DR

This paper investigates how adding long-range connections to regular networks affects flow efficiency, finding optimal conditions depend on the cost and geographical constraints of these connections.

Contribution

The study introduces a model linking flow properties to the probability and conductance of long-range links in small-world networks, revealing optimal parameters for maximum flow.

Findings

Maximum flow occurs at zero cost and randomness in long-range links.

Conductance increases with the exponent when costs are high.

Optimal exponent matches that for decentralized navigation in small-world networks.

Abstract

The small-world property is known to have a profound effect on the navigation efficiency of complex networks [J. M. Kleinberg, Nature 406, 845 (2000)]. Accordingly, the proper addition of shortcuts to a regular substrate can lead to the formation of a highly efficient structure for information propagation. Here we show that enhanced flow properties can also be observed in these complex topologies. Precisely, our model is a network built from an underlying regular lattice over which long-range connections are randomly added according to the probability, $P_{ij}\sim r_{ij}^{-\alpha}$, where $r_{ij}$ is the Manhattan distance between nodes $i$ and $j$, and the exponent $\alpha$ is a controlling parameter. The mean two-point global conductance of the system is computed by considering that each link has a local conductance given by $g_{ij}\propto r_{ij}^{-\delta}$, where $\delta$ determines the extent of the geographical limitations (costs) on the long-range connections. Our results show that the best flow conditions are obtained for $\delta=0$ with $\alpha=0$, while for $\delta \gg 1$ the overall conductance always increases with $\alpha$. For $\delta\approx 1$, $\alpha=d$ becomes the optimal exponent, where $d$ is the topological dimension of the substrate. Interestingly, this exponent is identical to the one obtained for optimal navigation in small-world networks using decentralized algorithms.