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Controllability and control of Complex Social Networks

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description


Dynamics and Control of Complex Social Networks

The main goals of the PhD project are to develop methods for assessing a level of the Complex Social Network controllability and to develop control mechanisms and analyse how they influence a Complex Social Network’s characteristics, structure, and dynamics (for details please see below).

There is a great interest in controlling of complex networks, including Complex Social Networks (CSNs), as this has great potential to make a big impact on tackling important societal challenges. There have been attempts to use control theory to approach this research challenge, however, due to the heterogeneous nature of social networks and their non-linear character, neither the network model nor the dynamics can be analytically described. This poses severe constraints on controllability of CSNs. To address these issues, there is an urgent need for extensive research into the CSNs’ controllability and suitable control mechanisms that would enable to direct the evolution of such networks in a desired way.

The fundamental research in the area of controlling complex networks, which is the main focus of this project, has a wide range of applications, one of them being changing human behaviour. Gender imbalance, minority marginalisation, and organised criminal behaviour, which are the main case studies to be developed within this project, represent only few challenges that modern society faces.
It is now for the first time in the human history, that we have the possibility to process big social data about the variety of interactions and activities of millions of individuals that can be represented as a CSN. It represents an increasingly important resource in the process of understanding behaviour of individuals, groups and whole communities yet there is no coherent and comprehensive approach (i) to analyse and model complex social networks, (ii) together with their non-linear dynamics and (iii) evolution, both triggered by natural dynamic processes occurring in such networks and the control mechanisms introduced to the network. All three components are crucial to advance our understanding of continuously changing people’s behaviour and propose mechanisms that help to change this behaviour if desired or necessary.

Networked systems and their evolution are usually analysed by building models of interactions including random, scale-free, and small-world models. However, these do not reflect the complex nature of real-world CSNs and related processes with high enough accuracy for their effective control. Building on the previous related research the proposed transformative route forward to overcome the limitations of existing techniques for CSNs control is, therefore, in developing novel high accuracy data-driven social network simulation models with ability to accurately model and take into account the inherent CSN dynamics as well as the dynamic behaviour induced by the applied, proposed control mechanisms.

Thus, the main goal is to build a robust and adaptive framework (DynaCo) that will address the following objectives:
● Objective 1 (OB1): To build robust models of Complex Social Networks (CSNs) taking simultaneously into account their structure, dynamics and control;
● Objective 2 (OB2): To develop methods for assessing a level of the CSN controllability;
● Objective 3 (OB3): To develop control mechanisms and analyse how they influence a CSN’s characteristics, structure, and dynamics;

This PhD project is mainly associated with OB2 and OB3 of this Dynamics and Control of Complex Social Networks project (ARC DP190101087).

The Candidate
Please apply if you have:
• MSc in Computer Science, Statistics, Mathematics or related field.
• Very good programming skills: Python, Scala, or any other programming language suitable for large scale data analysis
• Knowledge about: data analysis, graph theory, network science, social networks, control theory, agent-based modelling

About the Faculty

The Faculty of Engineering and Information Technology at UTS is a world-class faculty with a growing reputation for its quality and impact. Our research is highly advanced, industry-focused and part of the lively and rigorous research culture at UTS.

Focused on ’practical innovation’, our researchers are pioneering research solutions with real-world impact. They’re recognised leaders in their fields, responsible for delivering new, better and more cost-effective innovative solutions to current national and international challenges.

About the University

UTS is a dynamic and innovative university in central Sydney. One of Australia’s leading universities of technology, UTS has a distinct model of learning, strong research performance and a leading reputation for engagement with industry and the professions.

Funding Notes


Scholarship valued at $27,082 pa (indexed annually) for 3 years and is tax-free

Closing date for next intake
Australian Domestic students: 30th April 2019 (for commencement July 2019) or 30 September 2019 (for commencement January 2020)

Top-up scholarships, up to a maximum value of $12,000pa (for 3 years), may also be allocated to exceptional domestic candidates awarded competitive scholarships.

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