# 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.

## Programs

Programs where the course is taught: