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

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.

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