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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

Pedro José dos Santos Palhinhas Mota

Hours

Weekly - 4

Total - 70

Teaching language

Português

Prerequisites

Available soon

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.

Evaluation method

There is a continuous assessment in the course. It is composed of 2 tests classified from 0 to 20 and the continuous assessment grade is the average of both results. 

For those who fail there is still the possibility of a global exam.

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.