Undergraduate Course: Social and Technological Networks (INFR11124)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Networks are at the heart of modern technologies: Search engines, social networking sites and many others. In this course, we will study the properties of social networks, world wide web, Internet etc. We will cover the fundamental theories and techniques for analysing large networks and study recent developments in this area. The course will involve study of relevant theory, development of algorithms and writing programs to analyse real networks. |
| Course description |
Indicative set of topics (these do not correspond 1-1 to lectures)
* Introduction: Network analysis, simple examples
* Strong and weak ties, triadic closure, betweenness measures
* Definitions and properties of Random graphs, growth
* Preferential attachment & power law degree distributions
* Small world models
* Pagerank, HITS & structure of the web
* Spectral graph theory and applications
* Community detection & Clustering
* Cascades and epidemics
* Influence maximization
* Other current topics
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | Good programming skills (preferably python or java or C++, reading and writing files, programming basic algorithms). Basic Knowledge of Linear Algebra (matrix operations, eigen vectors and eigen values, orthogonality, Linear independence, vector spaces). Data structures and algorithms (asymptotic notation, time and space complexity, divide and conquer, sorting, basic graph theory, graph algorithms - spanning trees, network flows), probability (basic discrete probability & distributions, expectations), calculus (differentiation, integration). |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2017/18, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 16,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
80 )
|
| Assessment (Further Info) |
Written Exam
60 %,
Coursework
40 %,
Practical Exam
0 %
|
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | | 2:00 | |
|
| Academic year 2017/18, Part-year visiting students only (VV1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 16,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
80 )
|
| Assessment (Further Info) |
Written Exam
60 %,
Coursework
40 %,
Practical Exam
0 %
|
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate critical understanding of principal concepts in the subject of properties of large networks.
- Apply concepts and techniques that are at the forefront of network science
- Undertake autonomous small projects in this area, with responsibility for own work, planning and execution.
- Develop original and creative responses to problems; apply critical analysis and synthesis to forefront issues in network analysis
- Critically review and evaluate own work and that of others in the area of network analysis; communicate one¿s understanding and analysis in a concise manner.
|
Reading List
M. Newman. Networks, an introduction.
Leskovec, Rajaraman, Ullman. Mining of Massive Datasets.
Easley, Kleinberg. Networks, Crowds and Markets: Reasoning about a highly connected world.
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Additional Information
| Course URL |
http://www.inf.ed.ac.uk/teaching/courses/stn |
| Graduate Attributes and Skills |
Not entered |
| Keywords | Algorithms,computer Networks,social networks,graph theory,randomized algorithms,Data mining |
Contacts
| Course organiser | Dr Rik Sarkar
Tel: (0131 6)50 4444
Email: Rik.Sarkar@ed.ac.uk |
Course secretary | Mr Gregor Hall
Tel: (0131 6)50 5194
Email: gregor.hall@ed.ac.uk |
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