Postgraduate Course: Methods for the Analysis of Networks (INFR11083)
Course Outline
School  School of Informatics 
College  College of Science and Engineering 
Course type  Standard 
Availability  Available to all students 
Credit level (Normal year taken)  SCQF Level 11 (Postgraduate) 
Credits  10 
Home subject area  Informatics 
Other subject area  None 
Course website 
http://www.inf.ed.ac.uk/teaching/courses/man 
Taught in Gaelic?  No 
Course description  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. 
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. 
Additional Costs  None 
Information for Visiting Students
Prerequisites  None 
Displayed in Visiting Students Prospectus?  Yes 
Course Delivery Information

Delivery period: 2011/12 Semester 2, Available to all students (SV1)

WebCT enabled: No 
Quota: None 
Location 
Activity 
Description 
Weeks 
Monday 
Tuesday 
Wednesday 
Thursday 
Friday 
Central  Lecture   111  11:10  12:00      Central  Lecture   111     11:10  12:00  
First Class 
Week 1, Monday, 11:10  12:00, Zone: Central. AT M1 
Exam Information 
Exam Diet 
Paper Name 
Hours:Minutes 


Main Exam Diet S2 (April/May)   2:00   
Summary of Intended 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. 
Assessment Information
Written Examination 70
Assessed Assignments 30
Oral Presentations 0
Assessment
There is a single written exam, which accounts for 70% of the final grade. In addition there are two pieces of coursework which together account for the remaining 30%. In one of these students will be required to look for data of their own and to conduct an analysis employing appropriate techniques as introduced in the course. The other will involve giving a short presentation of a current research paper. Students will have sufficient choice to use this, if they wish, to reinforce their MSc specialism, such as Computational Neuroscience.
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year. 
Special Arrangements
None 
Additional Information
Academic description 
Not entered 
Syllabus 
+ 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 
Transferable skills 
Not entered 
Reading list 
none 
Study Abroad 
Not entered 
Study Pattern 
Lectures 20
Tutorials 0
Timetabled Laboratories 0
Nontimetabled assessed assignments 20
Private Study/Other 60
Total 100 
Keywords  Not entered 
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 2701
Email: Kate.Weston@ed.ac.uk 

