Large Graph Analytics

Objectives

The main goal of this course is to develop skills for studying structures of large graphs. The approaches to achieve this are covered by topics 3 and 4, after an introduction (point 2) to the necessary basics of graphs. In point 1, as a motivation for the course, large graphs that occur in a number of contexts are presented and particularities are pointed. Points 4 and 5 provide methodologies to predict the evolution of phenomena over objects that are represented as large graphs.

General characterization

Code

12082

Credits

6.0

Responsible teacher

Jorge Orestes Lasbarrères Cerdeira

Hours

Weekly - 4

Total - Available soon

Teaching language

Português

Prerequisites

Students should have some skills on formulation problems using integer variables, and on algorithms. 

Bibliography

Networks (second edition), Mark Newman, Oxford University Press, 2018 (ISBN: 9780198805090)

Networks, Crowds, and Markets: Reasoning about a highly connected World, David Easley and Jon Kleinberg, Cambridge University Press, 2010 (ISBN: 9780521195331)

Graph Analysis and Visualization: Discovering Business Opportunity in Linked Data, Richard Brath and David Jonker, Wiley, 2015 (ISBN: 978-1-118-84584-4)

Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph, David Loshin, Imprint: Morgan KaufmannPrint, Elsevier, 2013 (ISBN: 978-0-12-417319-4)

Teaching method

Classes are theoretical/practical consisting of exposition and discussions of concepts and methodologies complemented with examples and problems proposed for solving.

The student will be excluded of the evaluation if his presences in classes is less than 2/3 of the total number of classes.

The student can be evaluated by one test assessment, and a written and oral presentation of a study on a topic to be proposed. Student will be approved if the results obtained in the two evaluation elements sum up (rounded) at least 10. The grade will be the rounded sum.

The student can also be approved by a final exam if the exam''''s grade is at least 10. The grade will be the one attained in the exam (rounded).

Evaluation method

The student will be excluded of the evaluation if his presences in classes is less than 2/3 of the total number of classes.

The student can be evaluated by a test assessment, scoring 12, and a written and oral presentation of a study on a topic to be proposed, scoring 8. Student will be approved if the results obtained in the two evaluation elements sum up (rounded) at least 10. The grade will be the rounded sum.

The student can also be approved by a final exam if the exam''''s grade is at least 10. The grade will be the one attained in the exam (rounded).

Subject matter

1 Examples of large real graphs;

2 Basic graph concepts;

3 Topological measures (centrality, communities, similarity);

4 Large scale structure (components, shortest paths and small-world effect, vertices degree distribution, centrality measures distribution);

5 Random graphs;

6 Processes over large graphs.

Programs

Programs where the course is taught: