Course Overview

Many systems in the real world are already organized in networks, for example, electricity transmission and distribution networks, road networks, social networks, computer networks, and neural networks. With the growth of these networks, the science and engineering deal with more and more problems modeled by complex networks (large sparse graphs). Thus, the study of complex networks is important and of general interests to various scientific areas. In computer science, complex networks can be applied to various research fields, such as, data mining, image processing, information retrieval, pattern recognition, bioinformatics and grid computing. With the in-depth study of the theory of complex networks, we can obtain a basis for the development of research in complex network field it own, in computer science, as well as in engineering and other sciences. Due to the broad interests and wide range of applications of complex networks, we intend to offer this course to all areas of computer science and computational mathematics.

Learning Achievement

Presenting to the students the basic and intermediate levels of techniques for complex network analysis, as well as presenting network modeling methods for solving real computational problems involving complex networks.

Competence

Course prerequisites

Grading Philosophy

Evaluation: 01 written test and 02 practical tasks. The final grade will be calculated by the weighted average of the test and the practical tasks.

Course schedule

The aim of this course is to explore the concepts, techniques and applications involved in complex networks. 1) Introduction: Basic Concept of Complex Networks; Evolution of Complex Networks; 2) Complex Networks Models and Generation Algorithms: Random Networks; Small-World Networks; Scale-Free Networks; Clustered Networks; 3) Complex Network Measures: Centrality; Connectivity; Transitivity; Assortativity; Local Density ; Betweenness; Other Measures; 4) Advanced Network Analysis Techniques: Searching Methods for Complex Networks; Graph Isomorphism and Networks Similarity; Flow Optimization in Complex Networks; Community Detection in Complex Networks; Spectrum Analysis; Generating Functions; Other Techniques; 5) Applications: Data Mining; Machine Learning; Information Retrieval; Image Processing and Pattern Recognition; Grid Computing; Network Security; Bioinformatics; Other Applications;

Course type

Online Course Requirement

Instructor

Antonio Carlos Roque da Silva Filho, Alexandre Souto Martinez, Zhao Liang

Other information

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