Coventry University Featured PhD Programmes
University of Southampton Featured PhD Programmes
University of Oxford Featured PhD Programmes

Complex network analysis and resilience in large-scale multi-layer networks (Application Ref: SF19/EE/CIS/SHANG)

Faculty of Engineering and Environment

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
Dr Y Shang , Prof A Bouridane Applications accepted all year round Self-Funded PhD Students Only

About the Project

The goal of this project is to investigate and develop new data science approaches to model large-scale networks. Recent advances in network science have enabled researchers to understand the resilience of large-scale topological systems and develop algorithms to improve their robustness in the presence of different classes of failures and perturbations. This project aims to further enhance the understanding of large-scale complex systems and create a platform to translate fundamental research into real-world impact.

Multilayer networks are emerging as a powerful paradigm for describing various real networked systems characterized by the coexistence of different types of interactions or coupling among disparate types of networks. For example, global infrastructures are formed by several interdependent networks such as power grids, water supply networks, and communication systems. Similarly, the complexity of the brain is encoded in the different nature of the interactions existing at the functional and the structural levels.

In this project we aim to investigate coupled multiplex infrastructures that span both urban cores and rural peripheries. A targeted outcome of the project will be to create a data-driven modelling framework that can support and inform stakeholders in many different ways including (i) quantifying and understanding the stability of critical systems (ii) prioritizing resilient investments, (iii) developing resilient adaptive algorithms for cyber-physical systems, and (iv) educating and informing the public about risk, uncertainty, and resilience. Random graph models and numerical simulations will be used.

At Computer and Information Sciences in Northumbria, access to excellent learning and teaching environment for students and staff is available. We bring researchers from mathematics and computer science with skills in statistics, machine learning, and data analytics and distributed computing and seek industrial collaboration to work together in an open and collaborative environment with a shared goal to generate world-class academic research in data science leading potentially to societal impact.

This project is supervised by Dr Yilun Shang.

Eligibility and How to Apply:

Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.

For further details of how to apply, entry requirements and the application form, see

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF19/EE/CIS/SHANG) will not be considered.

Start Date: 1 March 2020 or 1 October 2020

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.

Funding Notes

This is an unfunded research project.


Y. Shang, Subgraph robustness of complex networks under attacks, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49, 821—832.

S. Al-Maadeed, R. Boubezari, S. Kunhoth, A. Bouridane, Robust feature point detectors for car make recognition, Computers in Industry, 2018, 100, 129—136.

A. Tyra, J. Li, Y. Shang, S. Jiang, Y. Zhao, S. Xu, Robustness of non-interdependent and interdependent networks against dependent and adaptive attacks, Physica A, 2017, 482, 713—727.

FindAPhD. Copyright 2005-2020
All rights reserved.