Link deletion in directed complex networks

TL;DR

This paper systematically investigates how directed networks withstand random and targeted link removals, analyzing the impact of network features like clustering and assortativity on their robustness.

Contribution

It provides a comprehensive analysis of directed network robustness under various attack strategies, considering both synthetic models and real-world networks.

Findings

Robustness varies with network topology and attack strategy.

Clustering and degree correlations influence network resilience.

Targeted attacks based on centrality measures are more damaging.

Abstract

We present a systematic and detailed study of the robustness of directed networks under random and targeted removal of links. We work with a set of network models of random and scale free type, generated with specific features of clustering and assortativity. Various strategies like random deletion of links, or deletions based on betweenness centrality and degrees of source and target nodes, are used to breakdown the networks. The robustness of the networks to the sustained loss of links is studied in terms of the sizes of the connected components and the inverse path lengths. The effects of clustering and 2-node degree correlations, on the robustness to attack, are also explored. We provide specific illustrations of our study on three real-world networks constructed from protein-protein interactions and from transport data.