Growing Self-organized Design of Efficient and Robust Complex Networks

TL;DR

This paper introduces a biologically inspired, incremental network growth model that produces efficient, robust, and scalable complex networks with onion-like structures, suitable for resilient infrastructure in challenging environments.

Contribution

It proposes a novel growth mechanism combining copying and shortcut links to create networks with optimal robustness and efficiency, unlike traditional models.

Findings

Networks exhibit onion-like structure with high robustness.

Degree distribution is exponential-like without overloaded hubs.

Networks are efficient with short paths and adapt to environmental changes.

Abstract

A self-organization of efficient and robust networks is important for a future design of communication or transportation systems, however both characteristics are incompatible in many real networks. Recently, it has been found that the robustness of onion-like structure with positive degree-degree correlations is optimal against intentional attacks. We show that, by biologically inspired copying, an onion-like network emerges in the incremental growth with functions of proxy access and reinforced connectivity on a space. The proposed network consists of the backbone of tree-like structure by copyings and the periphery by adding shortcut links between low degree nodes to enhance the connectivity. It has the fine properties of the statistically self-averaging unlike the conventional duplication-divergence model, exponential-like degree distribution without overloaded hubs, strong robustness against both malicious attacks and random failures, and the efficiency with short paths counted by the number of hops as mediators and by the Euclidean distances. The adaptivity to heal over and to recover the performance of networking is also discussed for a change of environment in such disasters or battlefields on a geographical map. These properties will be useful for a resilient and scalable infrastructure of network systems even in emergent situations or poor environments.