BME VIK - Komplex hálózatok struktúrája és dinamikája

vissza a tantárgylistához nyomtatható verzió

Structure and Dynamics of Complex Networks

A tantárgy neve magyarul / Name of the subject in Hungarian: Komplex hálózatok struktúrája és dinamikája

Last updated: 2010. november 11.

Budapest University of Technology and Economics
Faculty of Electrical Engineering and Informatics

Mérnök informatikus szak

BSc képzés

Course ID	Semester	Assessment	Credit	Tantárgyfélév
VISZA093		3/1/0/v	4

3. Course coordinator and department Dr. Recski András,

Web page of the course www.cs.bme.hu/....

4. Instructors

Name	Position	Department
János KERTÉSZ	Professor	Department of Theoretical Physics
Péter CSERMELY	Professor	Department od Medical Chemistry, Molecular Biology and Pathobiochemistry, Semmelweis University

7. Objectives, learning outcomes and obtained knowledge

Complex systems consist of many interacting units and are characterized by nonlinearity, positive and negative feedback, and emergent cooperative phenomena. Examples are the brain, the cell, the internet, the economy etc. Recent developments in network science provide efficient tools to handle such systems. In the first part of the lectures we will present the basics of complex network theory, including small world and scale free properties, important measures, spreading and breakdown phenomena, weighted and directed graphs. In the second part examples will be analyzed from information technology, economy, sociology and biology.

8. Synopsis

Introduction

Complex systems

Hierarchical structures

Elements of percolation theory, critical phenomena, scaling

Fractals

Networks, basic concepts of graph theory

Properties of real networks: small-worldness, scale freeness, community structure, hierarchies

Centrality measures

Clustering, assortativity

Motifs

Characteristics of weighted networks

Correlation networks

Models

Erdős Rényi

Watts-Strogatz

Barabási-Albert

Configuration

Fitness-models, rich-gets-richer, Bose-Einstein condensation

Community detection

Modularity based methods

Local methods

Overlapping communities

Hierarchical clustering in weighted graphs

Benchmarks

Stability of networks

Error tolerance and vulnerability

Spreading phenomena, diseases, models, vaccination

Breakdown in a fuse network – the Big Blackout

Random walks on networks

Diffusion and Laplace operator

Transport phenomena

The Internet as a complex network

Statistical properties of the WWW

Wikipedia: The complex network of cooperative value creation

Social networks

Data collection, traditional and new (ICT-based) forms

Graphs representing social networks: binary, signed, weighted

The importance of weak ties

Email and mobile phone networks and what we can learn from them

Complex networks in economy and finance

Networks of companies

Networks of banks: The domino effect

Correlation networks and portfolio optimization

Cellular networks

Protein structures: localization of active centers, landscape of conformational changes, evolution

Protein-protein interaction networks: hub and module dynamics in cellular changes

Signaling and gene regulatory networks

Networks of neurons and ecosystems

Network construction from emergent behavior (reverse engineering) and from correlations

Changes in brain default networks during aging and disease

Ecological networks: keystone species and prediction of ecosystem damage

Networks and drug design

Human disease and drug-target networks

Determination of drug binding sites in amino acid networks

Prediction of novel drug targets by network analysis

9. Method of instruction Lectures, recitations, project-based computer assignments

10. Assessment The students are requested to participate actively in the exercises, where they can acquire up-to-date knowledge on the subject at the skill level. Exercises will be adjusted to the lectures and the students will get home work, which they have to solve in some cases individually, in other cases in team. The tasks include analytical problem solving, programming, data search, library/internet survey.

The grade is based on the exercises (60 %) and on a final test (40 %).

12. Consultations You can reach the instructors at the following e-mail address for consultation:

János KERTÉSZ: janos.kertesz@gmail.com

Péter CSERMELY: csermely@eok.sote.hu

13. References, textbooks and resources

A.L. Barabási: Linked (Perseus, 2002);

R. Albert and A.L. Barabási: Statistical Mechanics of Complex Networks, Rev. Mod. Phys. 74, 47-97 (2002); M.E.J. Newman, The structure and function of complex networks, SIAM Review, 45, 167-256 (2003);

S.N. Dorogovtsev and J.F.F. Mendes, Evolution of Networks: From Biological Nets to the Internet and WWW, Oxford University Press (2003);

R. Pastor-Satorras, A. Vespignani, Evolution and Structure of the Internet: A Statistical Physics Approach, Cambridge (2004);

M. O. Jackson: Social and Economic Networks, Princeton Univ. Press (2008);

P. Csermely: Weak Links: The Universal Key to the Stability of Networks and Complex Systems, Springer (2009).

14. Required learning hours and assignment

Number of contact hours	56
Preparation to the classes	20
Preparation to the tests
Homework	24
Assigned reading
Preparation to the exam	20
Total	120

15. Syllabus prepared by

Name	Position	Department
János KERTÉSZ	Professor	Department of Theoretical Physics
Péter CSERMELY	Professor	Department od Medical Chemistry, Molecular Biology and Pathobiochemistry, Semmelweis University

Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

Komplex hálózatok struktúrája és dinamikája