851-0585-04L – Modeling and Simulating Social Systems ......(seminar thesis) Handing in seminar...

2012-11-05 © ETH Zürich |

851-0585-04L – Modeling and Simulating Social Systems with MATLAB Lecture 7 – Simulations with Networks

Chair of Sociology, in particular of

Modeling and Simulation

Karsten Donnay and Stefano Balietti

2012-11-05 K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 2

Schedule of the course 24.09. 01.10. 08.10. 15.10. 22.10. 29.10. 05.11. 12.11. 19.11. 26.11. 03.12. 10.12. 17.12.

Introduction to MATLAB

Introduction to social-science modeling and simulations

Working on projects (seminar thesis)

Handing in seminar thesis and giving a presentation

2012-11-05 K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 3

Schedule of the course 24.09. 01.10. 08.10. 15.10. 22.10. 29.10. 05.11. 12.11. 19.11. 26.11. 03.12. 10.12. 17.12.

Introduction to MATLAB

Working on projects (seminar thesis)

Handing in seminar thesis and giving a presentation

Dynamical Systems (no-space) Cellular Automata (grid)

Networks (graphs)

Continuous Space (…)

Different ways of Representing space

2012-11-05 K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch

Goals of Lecture 7: students will 1.  Consolidate knowledge acquired during lecture 6, through

brief repetition of the main points 2.  Translate a research question into a simple model of

simulation on networks 3.  Get a firm grasp in algorithms to generate different network

topologies 4.  Review a custom implementation of algorithms to efficiently:

  Generate different networks topologies

  Compute statistical properties

5.  Run a simple simulation of the emergence of giant component in a random network

Repetition: Definition of Graph A graph consists of two entities:

  Nodes (vertices): N

  Links: L Edge: undirected link Arc: directed link

The graph is defined as G = (N,L)

Source: Batagelj

Repetition: Networks Topologies

  Random:   Small average path length   Small clustering coefficient (in general)

  Small World:   Small average path length   High clustering coefficient

  Scale Free:   Slightly smaller average path length   Highest clustering coefficient

Repetition: Networks Topologies

  Random:   Small average path length   Small clustering coefficient (in general)

  Small World:   Small average path length   High clustering coefficient

  Scale Free:   Slightly smaller average path length   Highest clustering coefficient

Homegeneous (Exponential) Degree Distr.

Power Law Degree Distr.

Examples of different network topologies

Source: Wang (2003)

From a problem to a scientific answer  Observation:

  The role of social networks in shaping the public opinion is becoming increasingly more important

From a problem to a scientific answer  Question:

  How easy is to be manipulated in a social network?

From a problem to a scientific answer   Literature Review:

  Was the problem already treated?

From a problem to a scientific answer   Literature Review:

  How existing models can be improved?

From a problem to a scientific answer  Creating a new model

  Let us assume ‘informed’ agents   Let us consider networks

  How easily can the opinion of other persons be drifted in online social netwrks?

https://github.com/msssm/Informed_Agents

From a problem to a scientific answer  Results

  Let us assume ‘informed’ agents   Let us consider networks

https://github.com/msssm/Informed_Agents

Generators  How to generate random, realistic graphs?

1.  Probabilistic generators

2.  Degree-based generators

3.  Process-based generators

Probabilistic Generators: Erdos-Renyi   Algorithm:

  Start with a number of nodes n (fully not connected)   Define probability of connection P   For all the possible couples of nodes a link is created

with probability P

  The average number of links is given by:   p*n*(n-1)/2

  The greater P the higher the average degree of the network

Probabilistic Generators: Erdos-Renyi

  random graph – 100 nodes, avg degree = 2

  Fascinating properties (phase transition)

  But: unrealistic (Poisson degree distribution != power law)

E-R model & Percolation

1 N->infty

K = avg(k) Pc = Prob( there is a giant connected component)

  The formation of the Giant Component is not a smooth process.

  It emerge all of sudden when

p > 1/n

  This phenomenon is called 1st order phase-transition

Graphs: Laws and patterns   Are real graphs random?   If we look at the data the answer most of the

time is: NO!!

degree degree

count count

Degree-based generators   Figure out the degree distribution (eg., ‘Zipf’)

  Assign degrees to nodes

  Put edges, so that they match the original degree distribution

Process-based: Preferential attachment   Algorithm:

  Start with a random connected graph   At each time step create a new node and attach it to

the others with probability:

  Ki = degree of node i

  That is: if a node has many links, it will get more in the future…

Process-based: Preferential attachment   Generates power-law tails (richer-get-richer)

  The degree distribution is a power law of the form: P(K) ~ k-3

Process-based: Preferential attachment   Generates power-law tails (richer-get-richer)

  The degree distribution is a power law of the form: P(K) ~ k-3

But still, it does not reproduce the property of shrinking diameter in real evolving networks…

Get the lecture files from GitHub

  Lecture files are also available on a GitHub repository:

  git@github.com:msssm/lectures_files.git

Get the lecture files from GitHub

git clone git@github.com:msssm/lectures_files.git

git pull

  If you have already uploaded your public key in GitHub (see live presentation),

 You can dowload the latest lecture files with the following command in GIT Bash

  to stay updated:

Lecture Files  Generate

  Random Networks   Small World Networks   Scale Free Networks

  Compute:   Average Path Length   Clustering Coefficient

  Export to file:   Parse an adjacency matrix and create an arc list   Write a cell array as a csv file

Lecture Files   Simulate the emergence of the giant component

in a random network (files online in .zip archive).   emergence.m   emergence_video.m   emergence_smooth.m

  See live demo.

R igraph  R and Matlab integration to plot nice

graphs (igraph library)

  https://github.com/tconring/Arabian-Spring

SOCNET Mailing List  Social Network Analysis Mailing List:

http://www.insna.org/pubs/socnet.html

Network Analysis Literacy Talk Tomorrow

  Why Pretty Woman is a good recommendation for somebody who loves Star Wars V. ?

  Prof. Katharina Zweig (Technical University Kaiserslautern, Germany)

  Tuesday, 13 Nov 2012, 11.00-12.00 ETH Zürich, Main Building, HG E 22

  http://www.sg.ethz.ch/

References   Jure Leskovec, Deepayan Chakrabarti, Jon M. Kleinberg, Christos

Faloutsos: Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication. PKDD 2005: 133-145

  Xiao Fan Wang and Guanrong Chen Complex Networks: Small-World, Scale-Free and Beyond

  Damon Centola, The Spread of Behavior in an Online Social Network Experiment. Science, Vol. 329 no. 5996 pp. 1194-1197 (2010)

  Duncan J. Watts, Steven H. Strogatz, Collective dynamics of 'small-world' networks. Nature 393, 440-442 (4 June 1998)

  Laszlo Barabasi, Scale-Free Networks: A Decade and Beyond. cience 24 July 2009: Vol. 325 no. 5939

  Laszlo Barabasi web site: http://nd.edu/~alb/

References   About the KONY phenomenon:

  http://invisiblechildren.com/kony/   http://globalspin.blogs.time.com/2012/03/19/kony-2012-mobs-

takedowns-and-meltdowns-but-very-little-truth/?xid=newsletter-europe-weekly

  http://www.youtube.com/watch?v=Y4MnpzG5Sqc

Projects   Today, there are no exercises. Instead,

you can work on your projects and we will supervise you.

851-0585-04L – Modeling and Simulating Social Systems ......(seminar thesis) Handing in seminar...

Documents

M.L. 2016, Chp. 186, Sec. 2, Subd. 04l Project Abstract ... · M.L. 2016, Chp. 186, Sec. 2, Subd. 04l Project Abstract. For the Period Ending September 30, 2018 . ... industrial waste

Telaire SM-UART-04L Laser Dust Sensor · SM-UART-04L Particulate Dust Sensor is designed for a wide range of air quality applications where fine particle dust needs to be measured

Seminar Thesis in E-Government Systems - unifr.ch€¦ · Seminar Thesis in E-Government Systems . University of Fribourg, Department of Informatics . eGovernment . Evaluation of

· Proposal Literature Review Graduate Seminar Thesis Research/Writing 7-9 30- 32 30- CHEM 580 Required Seminar 2 CHEM 598 Thesis CHEM 599 Electives Chemical Skills Graduate Seminar

ECON 495/499 HONOURS SEMINAR/HONOURS THESIS 2012 …faculty.arts.ubc.ca/asiwan/documents/econ495-2012-lecture-intro.pdf · HONOURS SEMINAR/HONOURS THESIS 2012-2013 CONTACT INFORMATION:

Thesis Template€¦ · Web viewTitle of Your Seminar Paper, Bachelor Thesis, or Master Thesis Subtitle Seminar Paper / Bachelor Thesis / Master T hesis Submitted:July 15th, 2005

Unud 353 2094604629 Thesis Perbaikan Print Seminar

Ngoc bich do thesis seminar book fall 2015

Master’s thesis agreement / Seminar thesis agreement for ...a12b3104-be9e-497e... · (Working) title of the Master’s thesis / Seminar thesis Number of ECTS Credits Planned submission

Final Thesis Seminar

The Fundamental Steps to Writing Thesis/Seminar … Fundamental Steps to Writing Thesis/Seminar Papers: ... submit a consent form ... For example Questions #4

Thesis seminar

Master’s thesis agreement / Seminar thesis agreement for ...3f5bd434-b478-418d... · (Working) title of the Master’s thesis / Seminar thesis Number of ECTS Credits Planned submission

My PhD Thesis seminar - April 2007

Thesis for master’s seminar in Peace and Conflict studies

Guidelines for theses, seminar papers and reports · Length of theses and seminar papers Master thesis 60 pages ± 10% Bachelor thesis 30 pages ± 10% Seminar paper, project report

Thesis Seminar - UC Berkeley College of Environmental Designced.berkeley.edu/downloads/academic/ARCH/arch_thesis_2015/klaja... · Thesis Seminar . Richard Klaja Fall 2014. Advisors:

851-0585-04L – Modelling and SimulatingModelling and ...webarchiv.ethz.ch/soms/teaching/MatlabSpring10/Lesson3.pdf · 851-0585-04L – Modelling and SimulatingModelling and Simulating

Master's Thesis Seminar — General Advice II

iDS-TP40-04L Traffic Incident Detection Server...iDS-TP40-04L Traffic Incident Detection Server User Manual 3 Safety Instructions Proper configuration of all passwords and other security