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Incentive Compatible Influence Maximization in Social Networks with Application to Viral Marketing

Mohite, Mayur and Narahari, Y (2011) Incentive Compatible Influence Maximization in Social Networks with Application to Viral Marketing. In: AAMAS '11 The 10th International Conference on Autonomous Agents and Multiagent Systems, 2011, Richland, SC.

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Official URL: http://dl.acm.org/citation.cfm?id=2034428

Abstract

Information diffusion and influence maximization are important and extensively studied problems in social networks. Various models and algorithms have been proposed in the literature in the context of the influence maximization problem. A crucial assumption in all these studies is that the influence probabilities are known to the social planner. This assumption is unrealistic since the influence probabilities are usually private information of the individual agents and strategic agents may not reveal them truthfully. Moreover, the influence probabilities could vary significantly with the type of the information flowing in the network and the time at which the information is propagating in the network. In this paper, we use a mechanism design approach to elicit influence probabilities truthfully from the agents. Our main contribution is to design a scoring rule based mechanism in the context of the influencer-influencee model. In particular, we show the incentive compatibility of the mechanisms and propose a reverse weighted scoring rule based mechanism as an appropriate mechanism to use.

Item Type: Conference Proceedings
Additional Information: Copyright of this article belongs to Association for Computing Machinery.
Keywords: Social Networks; Information Diffusion; Influence Maximization; Mechanism Design; Incentive Compatibility; Scoring Rules; Viral Marketing
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Id for Latest eprints
Date Deposited: 02 Mar 2013 05:09
Last Modified: 02 Mar 2013 05:09
URI: http://eprints.iisc.ac.in/id/eprint/45974

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