Information spread in social networks part 2
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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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Information spread in social networks part 2

Information spread in social networks (part 2)

Marin Stamov

CS 765

Nov 14 2011


Outline

Outline

  • Goals

  • My model

    • Believe

    • Trust

    • Tolerance

  • The simulation

  • Expected results

  • Conclusion


Goals

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


My model

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


Believe

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


Believe example

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


Trust

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


Tolerance

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


Road map

Road map

  • Goals

  • My model

    • Believe

    • Trust

    • Tolerance

  • The simulation

  • Expected results

  • Conclusion


The simulation

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


The simulation1

The simulation

B2`=50(T/100+1)(B1-B2)/100+B2

35

72

70

57

71

41

80

64

79

82

47

63

90


Expected results

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


Conclusion

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


References

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


Information spread in social networks part 2

Thank you!

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