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 | In this course, we will study core properties of networks arising in various social, scientific and technological contexts. We will see techniques for their analysis, and applications in social networks, World Wide Web, Internet, IoT etc. The course will cover fundamental theories and techniques from machine learning, algorithms and mathematics. We will see the relevance of these techniques in real networks, as well as use of network-based techniques in more general data analysis. The course will involve theoretical analysis in class, development of algorithms, and writing of programs to analyse network data. |
| Course description |
The course will study computational, mathematical and data analysis aspects of networks. Typical topics will include properties of social networks, epidemics, spread of innovation, random graphs, metric properties, preferential attachments and power law networks. It will cover relation to data analysis and machine learning: including clustering and community detection, submodularity, optimization, embedding (dimension reduction) and classification. Other current topics will be covered as appropriate.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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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 | This course is open to full year Visiting Students only, as the course is delivered in Semester 1 and examined at the end of Semester 2. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2019/20, Available to all students (SV1)
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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 )
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| Assessment (Further Info) |
Written Exam
60 %,
Coursework
40 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Visiting Students:
This course is open to full year Visiting Students only, as the course is delivered in Semester 1 and examined at the end of Semester 2. |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S2 (April/May) | | 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.
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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 | Miss Clara Fraser
Tel: (0131 6)51 4164
Email: clara.fraser@ed.ac.uk |
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