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

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    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