Postgraduate Course: Methods for the Analysis of Networks (INFR11083)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course studies methods for analysing the emergent properties of naturally-occurring networks. The past decade has brought overabundant quantitative information on various complex distributed systems, which one can often embed into a network presentation. Examples include: social networks, food webs, the world wide web, (natural) neural networks, metabolic and signalling networks. There is now a growing corpus of network-based methods and algorithms that can help build a bigger picture and approach questions about the design and global properties of such systems. One can in particular ask questions such as: how resilient is a network, how well does it propagate information (or diseases), how modular is it, what is a likely growth scenario, etc. These methods are sometimes given the catch phrase "network science" to stress their relative independence from the particulars of the network of interest --- which in itself is a remarkable fact. |
| Course description |
+ Various types of data and networks. - Typology of random graph models: Erdős-Rényi, scale-free, hierarchical. - Local properties: edge density, degree distribution, clustering coefficient, assortativity, motifs. - Global properties: path lengths, centrality, modularity/community. - Algorithms: clustering methods, power-law fitting, hierarchy reconstruction. - Examples of networks. One or more sample domain of application, taken from areas such as the following:
+ Protein networks: modular structures, propagation of copy number perturbations, phase transitions in structural protein networks.
+ Neural networks: spatial growth models, hierarchical structure, propagation of excitation, scaling across different species.
+ Social networks: propagation of innovation, citation, epidemics.
Relevant QAA Computing Curriculum Sections: Not yet available
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | For Informatics PG and final year MInf students only, or by special permission of the School. Students will need some familiarity with graph theory, algorithmics, elementary probability and elementary calculus. |
Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
| Not being delivered |
Learning Outcomes
1 - Recognise situations where data can be represented as a graph (directed, weighted or not, as appropriate).
2 - Explain how to compute the usual local or global metrics on large graphs.
3 - Given a large graph, identify the classes of random graphs of which it is likely to be a member.
4 - Given an existing network, identify possible associated growth scenarios.
5 - Describe the dynamics one can endow upon a graph and their asymptotic properties.
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Contacts
| Course organiser | Dr Michael Rovatsos
Tel: (0131 6)51 3263
Email: mrovatso@inf.ed.ac.uk |
Course secretary | Miss Kate Weston
Tel: (0131 6)50 2692
Email: Kate.Weston@ed.ac.uk |
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