Spreading processes on networks: who is the best spreader?

Introduction

Spreading is a ubiquitous process that describes many essential activities in society. Understanding how spreading occurs through social interaction networks is crucial for developing strategies to either prevent the spread of diseases or cascading failures and facilitate the rapid distribution of information. Identifying the best spreaders in a network is essential because these nodes (spreaders) are the most effective at amplifying the spread and maximizing the overall reach of the process. Over the years, scientists constantly introduce new metrics in order to measure specific features of specific graphs. This research will focus on the comparison of existing metrics and provide new insights into the influential node identification problem.

Project

The aim of this project is to analyze the differences between various node influence metrics specifically designed to identify the 'best spreader nodes'. You will study spreading processes and existing graph metrics for complex networks and conduct a systematic comparative analysis of their performance on real data.

Requirements

You are in the final stages of your degree in artificial intelligence, computer science, mathematics, electrical engineering or a similar degree. The successful student is expected to have strong programming skills, and have some background in Network Science. You are pragmatic and focused on making things work. Next to technical expertise we value high level of independence and ability to work remotely, communication skills and a results-driven attitude.

To apply

Contact Sergey Shvydun, This email address is being protected from spambots. You need JavaScript enabled to view it.

References

[1] Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., & Makse, H. A. (2010). Identification of influential spreaders in complex networks. Nature physics, 6(11), 888-893.

[2] Van Mieghem, P., Devriendt, K., & Cetinay, H. (2017), "Pseudoinverse of the Laplacian and best spreader node in a network", Physical Review E, vol. 96, No. 3, p 032311.

[3] Hernández, J. M., & Van Mieghem, P. (2011). Classification of graph metrics. Delft University of Technology: Mekelweg, The Netherlands.