Controlling self-organized criticality in complex networks

TL;DR

This paper introduces a control method for complex networks that reduces large avalanches in the Bak-Tang-Wiesenfeld model by targeting high-degree nodes, outperforming random strategies across different network types.

Contribution

The paper presents a novel control scheme that effectively suppresses large avalanches in complex networks by focusing on critical high-degree nodes, improving upon random control methods.

Findings

Targeted control reduces large avalanches more effectively than random control.

The strategy is effective across different network types, including real-world power grids.

Control reduces mass concentration, preventing critical cascade events.

Abstract

A control scheme to reduce the size of avalanches of the Bak-Tang-Wiesenfeld model on complex networks is proposed. Three network types are considered: those proposed by Erd\H{o}s-Renyi, Goh-Kahng-Kim, and a real network representing the main connections of the electrical power grid of the western United States. The control scheme is based on the idea of triggering avalanches in the highest degree nodes that are near to become critical. We show that this strategy works in the sense that the dissipation of mass occurs most locally avoiding larger avalanches. We also compare this strategy with a random strategy where the nodes are chosen randomly. Although the random control has some ability to reduce the probability of large avalanches, its performance is much worse than the one based on the choice of the highest degree nodes. Finally, we argue that the ability of the proposed control scheme is related to its ability to reduce the concentration of mass on the network.