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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

Maria Isabel Azevedo Rodrigues Gomes, 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

1 - Evaluation

Class approval can be obtained in one of two ways: Continuous assessment or Exam.

1.1 - Continuous assessment

Continuous assessment consists of 3 elements: 2 tests, with a maximum duration of 90 minutes each, and a group assignment.

Each test is scored out of 7 (seven) to 9 (nine) points and the group assignment is scored out of 4 (four) points. A student passes if the sum of all scores, rounded to the nearest whole number, is greater or equal to 10 points.

1.2 – Assessment by Exam

All students will be admitted to the written exam. The maximum time allowed for the exam is 3 hours. A student passes the exam if they score a minimum of 10 points on the exam.

FINAL NOTE: "Grade Improvement" requires proper enrollment in the Academic Division as outlined in FCT NOVA''s current grading policy.

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.