Analysis of Social and Information Networks

Code:

PRODEI040

Acronym:

ARSI

Keywords
Classification	Keyword
OFICIAL	Intelligent Systems

Instance: 2022/2023 - 2S

Active?	Yes
Web Page:	https://www.dcc.fc.up.pt/~pribeiro/aulas/arsi2223/
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Doctoral Program in Informatics Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
PRODEI	3	Syllabus	1	-	6	28	162

Teaching language

English

Objectives

Networks are a fundamental tool for modeling complex social systems (and others, such as biological systems). Having into account the emergence of large scale network data, this course focuses on the analysis of these networks, which provide multiple computational, algorithmic, and modeling challenges. The course will cover recent research on the structure and analysis of such networks, as well as models and algorithms that abstract their main properties.

Learning outcomes and competences

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.

Working method

Presencial

Program

- Introduction and Fundamentals: the emergence of network science; graph theory fundamental concepts; representing networks in computer; classical graph algorithms.

- Metrics and basic structural properties: degree distribution, paths and diameter, clustering coefficient, centrality measurements ((betweenness, closeness, eigenvector, ...).

- Network Visualization: graph drawing, layout algorithms, exploratory analysis with the aid of visualization.

- Common properties and network models: random networks and Erdös-Rényi model; “small-world” property and Watts-Strogatz model; “scale-free” property and Albert-Barabsi model; other models (ex: Kronecker graphs).

- Communities: algorithms for detecting communities; optimizing modularity; overlallping communities and other variants.

- Patterns and Subgraphs: subgraph as fundamental units; subgraph census; concept and algorithms for network motifs discovery; graphlet degree distributions; incorporating attributes such as colors and weights.

- Link Analysis: node rankings, HITS algorithms, PageRank and other variants.

- Propagation in networks: information flow; influence; epidemics and propagation models.

- Brief introduction to other topics: sampling; parallel algorithms; graph databases; link prediction; network alignment; node role analysis; temporal networks; multiplex networks; ...

Mandatory literature

David Easley and Jon Kleinberg; Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press, 2010. ISBN: 9780521195331 (http://www.cs.cornell.edu/home/kleinber/networks-book/)
Albert-László Barabási; Network Science (http://networksciencebook.com/)

Complementary Bibliography

Stanley Wasserman, katherine Faust; Social network analysis. ISBN: 978-0-521-38707-1
R. A. Hanneman and M. Riddle ; Introduction to social network methods, University of California, Riverside, 2005 (http://faculty.ucr.edu/~hanneman/)
Mark Newman; Networks, Oxford University Press. ISBN: 9780198805090

Teaching methods and learning activities

Lectures: exposition of selected topics and discussion of examples and case studies. Solving small problems with the application of the the given methodologies and using existing software. Implementing selected algorithms. Developing a network analysis project. Reviewing and presenting related scientific literature.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Trabalho escrito	50,00
Apresentação/discussão de um trabalho científico	20,00
Trabalho prático ou de projeto	30,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	54,00
Frequência das aulas	54,00
Trabalho de investigação	54,00
Total:	162,00

Eligibility for exams

N/A

Calculation formula of final grade

The final grade (FG) is calculated as follows:

FG = 30% x HW + 20% x AP + 50% x P

HW: Homework
AP: Scientific Article Presentation
P: Project

Special assessment (TE, DA, ...)

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 any oral presentations.

Classification improvement

It is possible to improve the classification in the next edition of the course.