Conférence thématique à la journée futur & ruptures de l'Institut Telecom http://www.fondation-telecom.org/evenement/journee-futur-et-ruptures-37/

1. Social Network Analysis
Challenges
Cécile Bothorel

2. Social Network Analysis
 User'spoint of view
 Analyst point of view
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3. An old field of Social Sciences
 1933 : sociogram by Jacob
Levy Moreno to map
relationships
 1954 : John A. Barnes
introduces "social network"
 1967 : Six degrees of
separation by Stanley
Milgram
 1973 : "The Strength of Weak
Ties" by Mark L. Granovetter http://fr.wikipedia.org/wiki/Fichier:Six_degrees_of_separation.png
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4. A new umbrella term: Complex Networks
 Macroscopic structure of
the web, late 1990s  Real data in a wide
• 2 Altavista crawls each with variety of sources
over 200 million pages and
1.5 billion links • Biology, physics,
finance, medecine,
transportation,
computer sciences
 What's in common?
Understand
• General properties,
structure, evolution,
propagation
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5. (very) big, rich and dynamic graphs
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6. (very) big, rich and dynamic graphs
 Now online social media
available
 A new computational
social science
 Challenges at every
steps
• Data management
• Model design
• Analysis : measures,
mining algorithms,
propagation, evolution
• Visualisation
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7. Data management
 Sources
 Acquisition
 Preparation
 Storage
 « Big data », cloud,
 Update
parellelization
 Privacy: anonymization not
enough? [Backstrom et al. 2007]
 Data owners do not know
how to exploit... and manage
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8. Model
 Explicit/implicit
connections
 Types of relationship
 Details interactions
 Profiles
 …..
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9. Analysis - measures
 Lots of measures
 But which ones?
Prestige
Influence
Centrality
 Marketing KPI:
Relevance
PageRank
• Value of Rts in Twitter?
• Value of a fan Facebook?
 Expert-finding
 E-Reputation
 Recommendation, social Shopping
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10. Analysis – community detection
[Fortunato 2010, Pons 2007]  Hierarchical algorithms
 Modularity
maximisation
 Spectral partitioning
 Evaluation?
 What is a good
partition?
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11. Analysis – community detection
 Structural partition only
 What about Augmented
Social network?
• Additional information:
Users profile, shared
contents, opinions, etc.
 Network Dynamics?
• Graph evolution [Leskovec
et al. 2005, 2008]
• Link prediction [Liben-
Nowell and Kleinberg 2003]
[Cruz et al. 2010, 2011 Thèse Futur&Ruptures]
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12. Analysis – propagation, virality
 Influence - information
propagation
 Maximizing the spread
of influence
 Influence may come from
outside the monitored data
 Part of influence-based
contagion and homophily-driven
[Easley and Kleinberg 2010] diffusion? Which factors affects
[Leskovec et al. 2006, 2007] the propagation ?
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13. Visualisation – for the end-user
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14. Visualisation – for the analyst
 Open Tools: Gephi, Tulip, iGraph
 Plenty of measures, algorithms
 Volume of Data?
Artists in the last.fm database, by Tamas Nepusz
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15. Visualisation – for the analyst
 New layouts?
 Here: Analyse the interaction
between communities [Cruz et al. 2012,
Thèse Futur&Ruptures]
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16. Visualisation – for the analyst
 Augmented social network exploration
• Compare perspectives
• Differents role assignation
[Cruz et al. 2012, Thèse Futur&Ruptures]
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17. Conclusion
Social network analysis: a renewed research area...
… to be challenged.
Various open questions & difficulties.
With sample of our contribution: Augmented Social Network
Analysis, Futur&Ruptures PhD thesis, Institut Telecom
http://http://labocommunicant.net/ppc/cecile-bothorel/
http://http://perso.telecom-bretagne.eu/cecilebothorel/
http://twitter.com/#!/CecileBothorel
[F. Bonchi et al. 2011, Social network analysis and Mining for Business
applications, ACM Trans. Intell. Syst. Technol.]
Telecom Bretagne, LUSSI Journées Futur & Ruptures, 26 January 2012