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Link Prediction in Multi Modal Social Networks

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									 In the name of God




Course Title:
Social Networks

Instructor: Masoud      Asadpour
Lecture: Introduction
Nx1<1xN
The Wisdom of Crowds
n    (Surowiecki,2004), these key criteria separate
      wise crowds from irrational ones:
      ¨  Diversity of opinion: Each person should have private
          information even if it's just an eccentric interpretation of
          the known facts.
      ¨  Independence: People's opinions aren't determined by
          the opinions of those around them.
      ¨  Decentralization: People are able to specialize and
          draw on local knowledge.
      ¨  Aggregation: Some mechanism exists for turning
          private judgments into a collective decision.
Examples of Networks
n    Brain interconnectivity map
n    Ecosystem and food chains
n    Gene, protein, molecule, and virus networks
n    Political relations and Lobbies
n    Companies and their financial relations
n    Bank transactions
n    Power lines, phone lines
n    Roads, highways, airlines, railways
n    Paper citations
n    Emails, Web, Internet
n    Mobile, SMS and Phone networks
n    Online friendship and social networks
A friendship network on
facebook
     Friendship network of children in
     a US school
                                      Yellow - White Race
                                      Green - Black Race
                                      Pink - Other




http://www.visualcomplexity.com/vc/
    Hierarchical structure of the
    Internet




http://www.visualcomplexity.com/vc/project.cfm?id=476
     interconnected cities across the
     globe (by router configuration and not physical backbone)




http://www.chrisharrison.net/projects/InternetMap/
Iranian Censored Network
Influence Netwotk in
Balatarin.com
Poles in Balatarin.com
Road and Airlines Network
                     -




                         -
Genetic interaction network
Yeast protein-protein interaction
network
    Food Web




http://www.stanford.edu/group/song/woody_images.html
Related courses
n  SocialNetworks
n  Complex Networks
n  Dynamic Networks
n  Prerequisites
   ¨ Algorithms
             Math
   ¨ Discrete
   ¨ Graph Theory
Examples of Applications
n  Elections
n  Marketing (e.g. viral marketing)
n  Revolutions
n  Epidemics
n  Fraud detection
n  ...?
What makes Social Networks
interesting?
n  Astring of recent discoveries has forced us
  to acknowledge that amazingly simple and
  far-reaching natural laws govern the
  structure and evolution of all the complex
  networks that surround us
Why Network Science is important?
n  Reductionism:    to disassemble nature in
    order to understand it
n  After spending a lot to disassemble nature,
    now we almost know the pieces. But we are
    still far from understanding nature as a
    whole.
n  Today we recognize that nothing happens
    in isolation. Most events are inter-
    connected
Why Network Science is important?
n  Nowadays, power lies within the hands of
   the ones who know the network:
    ¨ Brokering
    ¨ Public relation
    ¨ Advertisement
    ¨ Communication
    ¨ …?

n  Inthis course you –hopefully- learn how to
   think network!
Books
n    Easley, Kleinberg, Networks, Crowds, and Markets
      Reasoning about a Highly Connected World, 2010
n    Barabasi, Linked the new science of networks, 2002
n    S. Wasserman and K. Faust, Social Network Analysis,
      1994
n    More readings:
      ¨  P.J. Carrington, J. Scott, S. Wasserman, models and methods in
          social network analysis, 2005
      ¨  Hanneman, Introduction To Social Networks Methods, 2005
      ¨  J. Scott, Social Network Analysis: A Handbook, 2000
      ¨  A. Degenne and M. Forse, Introducing Social Networks, 1999
      ¨  Books on Graph Theory, Random Graphs, …
Papers
n    Boccaletti et al, “Complex networks Structure
      and Dynamics,” 2006
n    Newman, “The structure and function of complex
      networks,” 2003
n    Costa et al, “Characterization of complex
      networks A survey of measurements,” 2008
n    Fortunato, Community detection in graphs, 2010
n    Arenas et al, “synchronization in complex
      networks,” 2008
Grade (subject to change)
n  40% Homework
n  30% final exam
n  30% final project


n  Slideswill be available on ece.ut.ac.ir/
    Classpages
n  Register in CECM to receive the HWs
Software you might work with:
n    Network Analysis Tool:
      ¨    Network Workbench
      ¨    Pajek
      ¨    UCI Net
      ¨    NetworkX
      ¨    Gephi
n    Graph library:
      ¨    Jung
      ¨    SNAP library (C++): large graphs
      ¨    BOOST graph library (C++): uses STL
n    Data mining:
      ¨    Clementine
Lectures (perhaps not in this order)
n    Random graphs, power law, small-world property, scale-
      free networks, and generative processes
n    Networks and measurements: centrality and prestige,
      degree, closeness, betweenness, information, rank
      centralities, etc
n    Structural balance, transitivity, clusterability, motifs,
      dyadic and triadic relations
n    Communities and cohesive subgroups, clique, n-cliques,
      k-plexes, k-cores
n    Structural equivalence, positions, roles, and blockmodels
n    Diffusion of Information, Failures and Epidemics in
      networks
n    Consensus, Synchronization, etc
Communities
Structural Equivalence
                                             A
             H                       G               B
     G               B
             A                       F               C
    F                C
                                         E       D
         E       D




             A   B       C   D   E       F       G
List of projects (Groups of 2 or 3
persons)

n  Social mobile games
n  Social networking on mobile platforms
n  Bluetooth networks
n  Facebook applications
n  Social networking plugin for FireFox
List of projects
n  Social Network Engine
n  Social Network Analysis Toolbox
n  Search Engine based on Social Networks
n  Search Engine Optimization
n  Analysis of a subset of Persian web
   ¨ Persian   blogsphere
   ¨ Political groups

n  Persian   WordNet, semantic web
List of projects
n  Web   content analysis
  ¨    Keyword extraction
  ¨    Blog post extraction
  ¨    Expertness measuring
  ¨    Topic Modeling
  ¨    Diffusion of information and news
  ¨    Trend analysis
  ¨    Impact of online contents on real world
List of projects
n    Simulation of social movements
n    Control of collective behavior
n    Simulation of financial transactions
      ¨    Anti-money laundering
      ¨    Fraud detection
n    Network Marketing
n    Socially spreading diseases
n    Collection or Analysis of real world social networks
      ¨    NA
      ¨    Handicapped people
n    You can suggest project…

								
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