|Responsible unit:||Department of Informatics Engineering|
|Course/CS Responsible:||Doctoral Program in Informatics Engineering|
|Acronym||No. of Students||Study Plan||Curricular Years||Credits UCN||Credits ECTS||Contact hours||Total Time|
Social media services have led to the emergence of huge amounts of user-generated content in the World Wide Web, which are valuable sources of information and business intelligence. Underlying these services are rich social structures comprising millions of individuals and organizations, who interact online every day through information and media sharing. Analysis of such social structures is fundamental for understanding users behaviour and network dynamics. The first part of the course covers the fundamentals in graph theory, social network analysis and visualization. The second part addresses structural and dynamical properties observed in large scale networks. The final part of the course presents several network mining applications.
At the end of the this course the students should be able to:
- explain key concepts and techniques in social network analysis;
- apply a range of techniques for characterizing network structure;
- define methodologies for analysing explicit and implicit networks in several application contexts;
- demonstrate knowledge of recent research in the area and exhibit technical writing and presentation skills.
The course is structured in three parts: (I) Introduction to Social Network Analysis, (II) Network Structure and Dynamics, and (III) Mining Social and Information Networks: Techniques and Applications.
Part I - Introduction to Social Network Analysis
1. Fundamentals of graph theory: Paths and connectivity. Distance and breadth-first search. Connected components.
2. Basic social network metrics: Degree, clustering coefficient, cohesion, density, centrality measures, clique-census.
3. Exploratory network analysis: Data collection, analysis and interactive visualization.
Part II - Network Structure and Dynamics
1. Community structure: Strength of weak ties, community detection and betweeness centrality. Homophily, selection and social influence. Modularity. Graph partitioning methods.
2. The small-world phenomenon: Clustering. Milgram's small world experiment. Structure and randomness. Small world models.
3. Power laws and preferential attachment phenomena: Popularity as a network phenomenon. Rich-get-richer models and the effect of recommendation systems.
4. Cascading behaviour in networks: Cascades and clusters. Diffusion and the role of weak ties. Knowledge, thresholds, and collective action.
Part III - Mining Social and Information Networks: Techniques and Applications
1. Social web mining case studies: Analysis of explicit and implicit user interaction networks, semantic networks, folksonomies.
2. Business intelligence: Information extraction and sentiment analysis of social media streams.
3. Influence detection and expert finding: Measures of user influence. Identification of user roles and topic experts in online communities.
The course is composed by theoretical-practical classes, discussions, student
assignments and presentations. The student evaluation is based on the following
1) Homework assignments (HW)
2) Project + research paper (PP)
3) Oral presentation (OP)
|Elaboração de projeto||54,00|
|Frequência das aulas||54,00|
|Trabalho de investigação||54,00|
The final grade (FG) is calculated as follows:
FG = 20% x HW + 70% x PP + 10% x OP
HW: Homework assignments
PP: Project + research paper
OP: Oral presentation
Students in special conditions are not required to attend the class, but will have to submit the assignments and final project on the same dates as the ordinary students. Special sessions can be arranged if needed for the final oral presentation.
It is possible to improve the classification in the next edition of the course.