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 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. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
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. |
| Additional Costs | None |
Information for Visiting Students
| Pre-requisites | 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 | | 1-11 | 11:10 - 12:00 | | | | | | Central | Lecture | | 1-11 | | | | 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: 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 |
| Transferable skills |
Not entered |
| Reading list |
none |
| Study Abroad |
Not entered |
| Study Pattern |
Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled 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 |
|
© Copyright 2011 The University of Edinburgh - 16 January 2012 6:17 am
|
University Homepage