Improving the robustness of online social networks: A simulation approach of network interventions

TL;DR

This paper introduces a simulation-based approach to enhance the robustness of online social networks by modeling user behavior and implementing targeted interventions to prevent large-scale user drop-out cascades.

Contribution

It proposes a novel agent-based model using coreness to measure robustness and develops strategies to influence key agents, reducing drop-out cascades in OSNs.

Findings

Coreness effectively quantifies OSN robustness.

Targeted interventions significantly reduce user drop-out cascades.

Strategies improve overall stability of online social networks.

Abstract

Online social networks (OSN) are prime examples of socio-technical systems in which individuals interact via a technical platform. OSN are very volatile because users enter and exit and frequently change their interactions. This makes the robustness of such systems difficult to measure and to control. To quantify robustness, we propose a coreness value obtained from the directed interaction network. We study the emergence of large drop-out cascades of users leaving the OSN by means of an agent-based model. For agents, we define a utility function that depends on their relative reputation and their costs for interactions. The decision of agents to leave the OSN depends on this utility. Our aim is to prevent drop-out cascades by influencing specific agents with low utility. We identify strategies to control agents in the core and the periphery of the OSN such that drop-out cascades are significantly reduced, and the robustness of the OSN is increased.