Information spread in social networks (part 2)

Marin StamovCS 765Nov 14 2011

OutlineGoals

My modelBelieveTrustTolerance

The simulation

Expected results

Conclusion

GoalsModel 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 otherDo we need large starting seed if for successful spread

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

BelieveRepresented 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 Believe0 50 100

Believe example

Believe

I am 90 % sure the president of our company will

be reelected

Disbelieve Believe

Based on my previous

believes and how much I trust him I

would say 75%

TrustThe 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

Willingness of the agent to change his current believes

Opinion confirmation should be accumulated

Useful for representing forceful agents

75

77

73

78

72

20

Road mapGoals

My modelBelieveTrustTolerance

The simulation

Expected results

Conclusion

The simulationSmall scale

Easier to visualize Monitor each step

Large scaleMay show different results

Test different network topologies

My simulation programWritten in c++ (QT)Works with .net filesCan represent graphically the network

The simulationB2`=50(T/100+1)(B1-B2)/100+B2

90

6347

57 71

35

79 82

72 70

80

41

64

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 timeCreate graphicsEach activated agent use one of his edges at each step

ConclusionWe 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[1] Daron Acemoglu,Asuman Ozdaglar, Spread of (Mis)Information in Social

Networks Games and Economic Behavior 7 (2010)[2] D. Acemoglu, Munther Dahleh, Ilan Lobel, 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] Lada Adamic, Co-evolution of network structure and content, NetSci (2011)[9] Andrea Apolloni, Karthik Channakeshava, Lisa Durbeck, A Study of Information

Diffusion over a Realistic Social Network Model, Computational Science and Engineering, (2009). CSE '09

Questions ?Thank you!