Control and propagation processes in multilayer networks

In today’s world, there is significant interest in controlling spreading processes in multilayer networks, as these have a substantial impact on various aspects of modern society. These include: controlling the spread of diseases (e.g., coronavirus), curbing extremist behaviors, limiting the dissemination of fake news, evaluating state control mechanisms in financial networks (interactions between financial institutions), and managing advertising, political, and social campaigns (e.g., raising environmental awareness or promoting healthy lifestyles).

Due to the heterogeneous and nonlinear nature of multilayer networks—where multiple relationships between nodes exist simultaneously—neither the network dynamics nor the spreading processes can be analytically described. This creates major challenges in controlling these processes. To address these challenges, extensive research into multilayer networks and spreading control mechanisms is required to effectively manage these processes.

Fortunately, we now have access to vast datasets representing diverse interactions and behaviors of millions of individuals, which can be modeled as multilayer networks. This allows us to analyze not only the dynamics of such structures but also processes like disease spread, opinion formation, or fake news propagation. However, there is currently no unified approach to understanding (i) how spreading processes function in multilayer networks, (ii) how they can be influenced, and (iii) how to control them. Addressing these aspects is essential to better understand human behavior and propose mechanisms to shape it when necessary.

Dynamic processes in networks are often studied using simplified models, which fail to capture the complexity of real-world multilayer networks. This project proposes overcoming these limitations by developing advanced simulation models with high accuracy, enabling precise modeling and analysis of spreading processes in multilayer networks. The main goal is to understand the mechanisms behind spreading processes in real-world multilayer networks and leverage this knowledge to influence and control them through the following objectives:

Develop and evaluate methods for influencing spreading processes in multilayer networks. Design mechanisms to control multilayer network structures. Create and assess methods for controlling spreading processes. Test proposed solutions in real-world multilayer networks. The project will be executed through four work packages:

Influencing spreading processes. Controlling network structures. Managing spreading processes. Data acquisition, use cases, and research application. This 36-month project involves collaboration with partners from Australia, the USA, and Sweden.

Duration: 01.03.2023 - 28.02.2026

Funding: 841 800 PLN