INFORMATION EPIDEMICS AND VIRAL CONTAGION  Dmitry Paranyushkin / www.noduslabs.com
Watch                                                    the video on                                                    v...
TYPES OF NETWORK   Scale-free - degrees distributed following power-law(a few, but significant # of well-connected and disc...
TYPES OF NETWORK Small world - special case of scale free – tightly-knit looselyconnected communities with short distance ...
TYPES OF NETWORKRandom - degrees distributed “normally” across the nodes    (most have an average number of connections)
S                 I               R             S                 I               S     S               I                R...
Watch                        the video on                        vimeo.com/36958670CONTAGION DYNAMICS      Message = Virus
“healthy”                               “infected”HOW DOES IT HAPPEN?Ideology, Trends, Collective action, Protest, Meme...
most “friends”         adopted a      trend, so the   blue node does   the same finally 1. INFORMATION CASCADES   Herd-like...
no connections between the nodes      many connections between the nodes= cascades not possible               = cascades c...
3. START WITH A GROUPRapid spread of disease within tightly connected communities can lead to an epidemic outbreak even if...
WHY?Because once the contagion is spread within the group, it will spread across super-network to the other groups (Ball 1...
Better than random nodes, but still not   Optimal - leave the same number    perfect - immunize random groups         of s...
99%? 10% IS ENOUGH.Committed 10% can change the opinion of the majority as long   as they persistently broadcast their mes...
* not too many!         4. BUILD SHORTCUTSScale-free networks with shortcuts are better in propagating,dense networks are ...
1. Amplitude of contagion increases with the higher number of random shortcuts (Cummings 2005; Kuperman 2001) 2. Small-wor...
5. FOCUS ON BROKERSThe nodes that connect different communities, are the best one  to target when spreading a message. (St...
Image: CC Laura Billings @ FlickR       6. MESSAGE = VIRUSThe message should have the capacity to replicate itself        ...
Watch                                                                              the video on                           ...
RECONTEXTUALIZEAcknowledge the mindset of the target group,        but bring in some novelty.
Against Putin Facebook group   The viral message should imply                                       “against Putin”,      ...
PRACTICAL APPLICATIONS      Facebook Promotion
PRACTICAL APPLICATIONS       Event Organization
1. Information Cascades(people should be talking to each other)2. Giant Component(most of the people should be connected t...
Ball, F. (1997). Epidemics with two levels of mixing. The Annals of Applied Probability, 7(1), 46–89. Institute of Mathema...
INFORMATION EPIDEMICS AND VIRAL CONTAGIONWe used Gephi for network analysis and visualization –           download it on w...
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Information Epidemics and Viral Contagion

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In this presentation we demonstrate how groups of people adopt a certain trend or fashion, how rumours propagate through networks, how to communicate information to a large group of people in the most efficient way, and how the framework of network analysis can be used to better understand how we communicate.
The full research is available on http://noduslabs.com

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  • Information Epidemics and Viral Contagion

    1. 1. INFORMATION EPIDEMICS AND VIRAL CONTAGION Dmitry Paranyushkin / www.noduslabs.com
    2. 2. Watch the video on vimeo.com/2035117 SOCIAL NETWORKThe nodes are the people, the connections are interactions between them (visualization by Gephi - www.gephi.org)
    3. 3. TYPES OF NETWORK Scale-free - degrees distributed following power-law(a few, but significant # of well-connected and disconnected)
    4. 4. TYPES OF NETWORK Small world - special case of scale free – tightly-knit looselyconnected communities with short distance between the nodes
    5. 5. TYPES OF NETWORKRandom - degrees distributed “normally” across the nodes (most have an average number of connections)
    6. 6. S I R S I S S I R S EPIDEMIC MODELSS: Susceptible, I: Infected, R: Removed/Recovered (Ball 1997; Newman 2002; Newman et al 2006; Watts 2002)
    7. 7. Watch the video on vimeo.com/36958670CONTAGION DYNAMICS Message = Virus
    8. 8. “healthy” “infected”HOW DOES IT HAPPEN?Ideology, Trends, Collective action, Protest, Meme...
    9. 9. most “friends” adopted a trend, so the blue node does the same finally 1. INFORMATION CASCADES Herd-like behavior, influenced by the others. Only when“conversion threshold“ is exceeded (Watts 2002; Hui et al 2010; Young 2002)
    10. 10. no connections between the nodes many connections between the nodes= cascades not possible = cascades can occur 2. GIANT COMPONENT Most nodes must belong to the same component for the global epidemics to occur (Watts 2002; Newman et al 2006)
    11. 11. 3. START WITH A GROUPRapid spread of disease within tightly connected communities can lead to an epidemic outbreak even if the links are loose
    12. 12. WHY?Because once the contagion is spread within the group, it will spread across super-network to the other groups (Ball 1997).
    13. 13. Better than random nodes, but still not Optimal - leave the same number perfect - immunize random groups of susceptibles in each group STRATEGIES OF RESISTANCELeave the number of susceptibles the same in each group, thus preventing the virus from spreading within and throughout.
    14. 14. 99%? 10% IS ENOUGH.Committed 10% can change the opinion of the majority as long as they persistently broadcast their message (Xie et al 2011)
    15. 15. * not too many! 4. BUILD SHORTCUTSScale-free networks with shortcuts are better in propagating,dense networks are better for cascades. (Kuperman 2001; Yan et al 2008)
    16. 16. 1. Amplitude of contagion increases with the higher number of random shortcuts (Cummings 2005; Kuperman 2001) 2. Small-world wirings (links between different communities) enhance network synchronization (Barahora & Pecora 2002). 3. Synchronization (simultaneous information cascades) are boosted if the links are made between the nodes of varying degree (Boccaletti 2006) 4. Assortative networks (well-connected nodes attract each other) are good in percolating (spreading the message further and maintaining the endemic contagion for a longer term period). Disassortative networks (nodes with varying degree connect together) are better in sync, but the contagion is periodic and short lived (Bragard 2007; Newman 2002)THE ETHICS OF PROMISCUITY Or how to make random connections, without driving your network crazy.
    17. 17. 5. FOCUS ON BROKERSThe nodes that connect different communities, are the best one to target when spreading a message. (Stonedahl 2010; Freeman 1997)
    18. 18. Image: CC Laura Billings @ FlickR 6. MESSAGE = VIRUSThe message should have the capacity to replicate itself across the network.
    19. 19. Watch the video on vimeo.com/33742762Rumours started on Twitter during the London riots were much more long-lived when started with a query, which in turn produced statements in support and opposition of the original statement. START WITH A QUESTION @someone “Is it true what BILD wrote that Angela Merkel disappeared?” #weird #politics #germany #shithappens
    20. 20. RECONTEXTUALIZEAcknowledge the mindset of the target group, but bring in some novelty.
    21. 21. Against Putin Facebook group The viral message should imply “against Putin”, not “protect animal rights” THE SAME PURPOSEThe message should reiterate the purpose that brings the target network together.
    22. 22. PRACTICAL APPLICATIONS Facebook Promotion
    23. 23. PRACTICAL APPLICATIONS Event Organization
    24. 24. 1. Information Cascades(people should be talking to each other)2. Giant Component(most of the people should be connected to each other, bring the “loners” in)3. Focus on Groups(better the more densely connected ones, 10% can be enough)4. Make Random Shortcuts(communication outside of one’s community, diversity of links)5. Information Brokers(people who connect different communities together)6. Message = Virus(the message should have the capacity to replicate itself) SUMMARY Information Epidemics and Viral Contagion
    25. 25. Ball, F. (1997). Epidemics with two levels of mixing. The Annals of Applied Probability, 7(1), 46–89. Institute of Mathematical Statistics.Retrieved from http://projecteuclid.org/euclid.aoap/1034625252Ball, F., Neal, P., & Lyne, O. (2010). Epidemics with two levels of mixing. MOdelling Complex Systems, University of Manchester. Institute ofMathematical Statistics. Retrieved from http://projecteuclid.org/euclid.aoap/1034625252Bastian, M.; Heymann, S.; Jacomy, M.; (2009). Gephi: An Open Source Software for Exploring and Manipulating Networks. Association for theAdvancement of Artificial IntelligenceFreeman, L. (1977). A Set of Measures of Centrality Based on Betweenness. SociometryVol. 40, No. 1 (Mar., 1977): 35-41Hui, C., Goldberg, M., Magdon-Ismail, M., & Wallace, W. A. (2010). Simulating the diffusion of information: An agent-based modelingapproach. International Journal of Agent Technologies and Systems (IJATS), 2(3), 31–46. IGI Global.Kuperman, M., & Abramson, G. (2001). Small World Effect in an Epidemiological Model. Physical Review Letters, 86(13), 2909-2912.Newman, M. E. J. (2002a). The spread of epidemic disease on networks.Newman, M. E. J. (2002b). Assortative mixing in networks. Physical Review Letters, 89(20), 5. American Physical Society. Retrieved fromhttp://arxiv.org/abs/cond-mat/0205405Newman, M. E. J., Barabasi, A.-L., & Watts, D. J. (2006). The structure and dynamics of networks. Princeton University Press. doi:10.1073/pnas.0912671107Stonedahl, F., Rand, W., & Wilensky, U. (2010). Evolving Viral Marketing Strategies. Learning.Watts, D. (2002). A simple model of global cascades on random networks. Proceedings of the National Academy of, 99(9), 5766-71.Xie, J., Sreenivasan, S., Korniss, G., Zhang, W., Lim, C., & Szymanski, B. K. (2011). Social consensus through the influence of committedminorities. Physical Review E, 84(1), 1-9.Yan, G., Fu, Z.-qian, Ren, J., & Wang, W.-xu. (2008). Collective Synchronization Induced by Epidemic Dynamics on Complex Networks withCommunities. Science And Technology, 0, 3-7.Young, H. P. (2002). The Diffusion of Innovations in Social Networks. Economy as an evolving complex system 3, 3(1966), 1-19. OxfordUniversity Press, USA. REFERENCES
    26. 26. INFORMATION EPIDEMICS AND VIRAL CONTAGIONWe used Gephi for network analysis and visualization –  download it on www.gephi.org We used NetVizz app by Bernhard Rieder to get Facebook data. More on www.noduslabs.com -Contact: Dmitry Paranyushkin | dmitry@noduslabs.com Twitter: @thisislikecom | Facebook: Nodus Labs

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