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Metadata integration in Networks analysis with unobserved edges

PGR-P-349

Key facts

Type of research degree
PhD
Application deadline
Ongoing deadline
Country eligibility
International (open to all nationalities, including the UK)
Funding
Non-funded
Supervisors
Dr Luisa Cutillo
Schools
School of Mathematics
Research groups/institutes
Statistics
<h2 class="heading hide-accessible">Summary</h2>

Networks arise naturally in many areas of life; supermarkets use networks of customers to propose specific deals to targeted groups; banks orchestrate a complex system of transactions between them and clients; terrorists organise themselves in networks spread across countries; media and social networks dominate our lives, and inside each living being genes express and co-regulate themselves via complex networks. Graphs are mathematical objects that can be used to describe networks in terms of a set of nodes (vertices) and their interconnections (edges). The goal of Network clustering is to provide a partition of the network into clusters or communities (groups) of related nodes. Many algorithms exist that can automatically infer such clusters under various assumptions and a range of validation measures can be used to determine the quality of the resulting partition and improve our understanding of the network structure. Most of these algorithms only consider the topology of the network under study, however, additional information about each node, i.e. metadata, is often available to us and we might be able to use this to further validate the partition and improve our understanding of the network structure. For example, in biological gene networks, proteins or cellular functions are expected to correlate to the gene clusters. As a further example, in a social network, such as Facebook, we expect gender or age to correlate to clusters of people. In addition to this, in many complex systems, the exact relationship between nodes is unobserved or unknown. In some cases, we may observe interdependent signals from the nodes, such as time series. We aim to use such signals to infer the missing relationships. This project will consider a range of approaches to explore the relation of networks metadata with a given network partition when network edges are unobserved. The automatic embedding of metadata alongside class label inference will be investigated using a hierarchical Bayesian modelling approach. The most important stage of this research will be to automatically model the mismatch between metadata, predicted edges and ground truth. The output of this research will have a high impact in the field of biomedical science, where there is the need for fast integration of partial information on biological samples and class prediction.

<h2 class="heading hide-accessible">Full description</h2>

<p>The earliest start date for this project is 1 October 2020.</p>

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="https://www.leeds.ac.uk/info/130206/applying/91/applying_for_research_degrees">University&#39;s website</a>. Please state clearly in the research information section&nbsp;that the research degree you wish to be considered for is &lsquo;Metadata integration in Networks analysis&rsquo; with unobserved edges as well as&nbsp;<a href="https://eps.leeds.ac.uk/maths/staff/5526/dr-luisa-cutillo">Dr Luisa Cutillo</a>&nbsp;as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<h2 class="heading">Funding on offer</h2>

<p>Self-Funded Students only</p>

<h2 class="heading">Contact details</h2>

<p>For further information regarding your application, please contact Doctoral College Admissions by&nbsp;email:&nbsp;<a href="mailto:EMAIL@leeds.ac.uk">m</a><a href="mailto:maps.pgr.admissions@leeds.ac.uk">aps.pgr.admissions@leeds.ac.uk</a>&nbsp;or by&nbsp;tel: +44 (0)113 343 5057.</p> <p>For further information regarding the project, please contact Dr Luisa Cutillo by email:&nbsp;&nbsp;<a href="mailto:L.Cutillo@leeds.ac.uk">L.Cutillo@leeds.ac.uk</a></p>


<h3 class="heading heading--sm">Linked funding opportunities</h3>