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Information spread in social networks (part 2)

Information spread in social networks (part 2). Marin Stamov CS 765 Nov 14 2011. Outline. Goals My model Believe Trust Tolerance The simulation Expected results Conclusion. Goals. Model that allows the agents to change their believe in the information truthfulness

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Information spread in social networks (part 2)

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  1. Information spread in social networks (part 2) Marin Stamov CS 765 Nov 14 2011

  2. Outline • Goals • My model • Believe • Trust • Tolerance • The simulation • Expected results • Conclusion

  3. Goals • Model that allows the agents to change their believe in the information truthfulness • Useful for testing the spreading of information such as rumors, expectations, prognosis and other uncertain kinds of information • How the network topology affects the spread and the average believe of the agents • How will believe and disbelieve interfere with each other • Do we need large starting seed if for successful spread

  4. My model • Spreading of a probability, based on connection trust • Uses weighted undirected network • Allows agents to be prejudiced • Different then threshold or binary like models

  5. Believe • Represented by a single number from 0 to 100 • Estimate the chance that this information is truthful • Disbelieve is also an information which can spread the network Disbelieve Believe 0 50 100

  6. Believe example Based on my previous believes and how much I trust him I would say 75% I am 90 % sure the president of our company will be reelected Disbelieve Believe Believe

  7. Trust • The weight of each edges (0 to 100) • How much the neighbor will affect our beleive • Difficult to obtain in the real world • Relationship between trust and information spread

  8. Tolerance • Willingness of the agent to change his current believes • Opinion confirmation should be accumulated • Useful for representing forceful agents 73 72 77 75 20 78

  9. Road map • Goals • My model • Believe • Trust • Tolerance • The simulation • Expected results • Conclusion

  10. The simulation • Small scale • Easier to visualize • Monitor each step • Large scale • May show different results • Test different network topologies • My simulation program • Written in c++ (QT) • Works with .net files • Can represent graphically the network

  11. The simulation B2`=50(T/100+1)(B1-B2)/100+B2 35 72 70 57 71 41 80 64 79 82 47 63 90

  12. Expected results • Improve the model based on the data from the simulations • What topologies are best and worst for good spread • How the average believe changes over time • Create graphics • Each activated agent use one of his edges at each step

  13. Conclusion • We spread information, but we measure the probability that the information is true based on each agent estimation • Believe is the most important parameter in this model • Trust of the connection is important for the calculating of the estimated believe, but other parameters can also be used • Threshold can be used on the values of believe and trust

  14. References [1] DaronAcemoglu,AsumanOzdaglar, Spread of (Mis)Information in Social Networks Games and Economic Behavior 7 (2010) [2] D. Acemoglu, MuntherDahleh, IlanLobel, Bayesian learning in social networks, Preprint, (2008) [3] A. Banerjee and D. Fudenberg, Word-of-mouth learning, Games and Economic Behavior 46 (2004) [4] V. Bala and S. Goyal, Learning from neighbours, Review of Economic Studies 65(1998) [5] A. Banerjee, A simple model of herd behavior, Quarterly Journal of Economics 107(1992) [6] S. Sreenivasan, J. Xie, W. Zhang, Influencing with committed minorities, NetSci (2011) [7] Cindy Hui, Modeling the Spread of Actionable Information in Social Networks, (2011) [8] LadaAdamic, Co-evolution of network structure and content, NetSci (2011) [9] Andrea Apolloni, KarthikChannakeshava, Lisa Durbeck, A Study of Information Diffusion over a Realistic Social Network Model, Computational Science and Engineering, (2009). CSE '09

  15. Thank you! Questions ?

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