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
  1. Introduction

     

Complex systems

 

Hierarchical structures

 

Elements of percolation theory, critical phenomena, scaling

 

Fractals

 
  1. 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

 

 
  1. Models

     

Erdős Rényi

 

Watts-Strogatz

 

Barabási-Albert

 

Configuration

 

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

 

.

 
  1. Community detection

     

Modularity based methods

 

Local methods

 

Overlapping communities

 

Hierarchical clustering in weighted graphs

 

Benchmarks

 

 
  1. Stability of networks

     

Error tolerance and vulnerability

 

Spreading phenomena, diseases, models, vaccination

 

Breakdown in a fuse network – the Big Blackout

 

 
  1. Random walks on networks

     

Diffusion and Laplace operator

 

Transport phenomena

 

 
  1. The Internet as a complex network 

     

Statistical properties of the WWW

 

Wikipedia: The complex network of cooperative value creation

 

 
  1. 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

 

 
  1. Complex networks in economy and finance

     

Networks of companies

 

Networks of banks: The domino effect

 

Correlation networks and portfolio optimization

 

 
  1. 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

 

 
  1. 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

 

 
  1. 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