Adaptive and robust approach for predictive modelling of dynamics and evolution of Complex Social Networks
For the first time in history, we have the possibility to process ‘big data’ (gathered in computer systems) about the interactions and activities of millions of individuals. Complex Social Networks (CSN), extracted from this data, are extremely difficult to analyse. So far, there is no coherent and comprehensive approach to analyse such networks which is crucial to advance our understanding of continuously changing people’s behaviour.
The main area of research in the proposed project is the predictive modelling of big social data represented in a form of complex social network. The main goal of this project is to build models that enable to predict the future structure of complex social networks and their characteristics. In order to achieve this goal, first it needs to be understood how CSNs evolve and how this evolution changes over. The analysis will be performed on global (the whole structure) and local (individual nodes) levels to better understand the rules that govern network dynamics.
The main objectives (OB) of the proposed research are:
OB1. Literature review in the field of complex networked systems and their dynamics – an interdisciplinary perspective.
OB2. To develop and test models of the CSN dynamics – analysis of both the structure and the features of connections and nodes in order to discover the patterns of interactions and the manner in which these characteristics change over time.
OB3. To develop and test models for predicting the evolution of CSN – the elements that will be taken into consideration while developing the methods for network evolution prediction are as follows (a) predicting the probability of forming/fading of a connection, (b) changing number of nodes within the network, (c) changing features of both nodes and relations. The created framework of models will be verified in the real-world environment. The datasets that will be used are networks created on the basis of data from such datasets as emails, fora, blogs, billing data, etc. Benchmark data can be found among others at: http://konect.uni-koblenz.de/ and https://snap.stanford.edu/data/
This project will result in foundational contributions to modelling of complex social networks which are currently missing.
How to apply: Applications are made via our website using the Apply Online button below. If you have an enquiry about this project please contact us via the Email NOW button below, however your application will only be processed once you have submitted an application form as opposed to emailing your CV to us.
Candidates for funded PhD studentship must demonstrate outstanding qualities and be motivated to complete a PhD in 3 years.
All candidates must satisfy the University’s minimum doctoral entry criteria for studentships of an honours degree at Upper Second Class (2.1) and/or an appropriate Master’s degree. An IELTS (Academic) score of 6.5 minimum (or equivalent) is essential for candidates for whom English is not their first language.
In addition to satisfying basic entry criteria, BU will look closely at the qualities, skills and background of each candidate and what they can bring to their chosen research project in order to ensure successful and timely completion.
Funded candidates will receive a maintenance grant of £14,000 (unless otherwise specified) per annum, to cover their living expenses and have their fees waived for 36 months. In addition, research costs, including field work and conference attendance, will be met.
Funded Studentships are open to both UK/EU and International students unless otherwise specified.