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 naturallyoccurring 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 networkbased 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: ErdősRényi, scalefree, hierarchical.  Local properties: edge density, degree distribution, clustering coefficient, assortativity, motifs.  Global properties: path lengths, centrality, modularity/community.  Algorithms: clustering methods, powerlaw 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

Entry Requirements (not applicable to Visiting Students)
Prerequisites 

Corequisites  
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
Prerequisites  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.

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 

