Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81971
Title: Profit Maximization for Viral Marketing in Online Social Networks
Authors: Tang, Jing
Tang, Xueyan
Yuan, Junsong
Keywords: Greedy algorithms
Approximation algorithms
Issue Date: 2016
Source: Tang, J., Tang, X., & Yuan, J. (2016). Profit maximization for viral marketing in Online Social Networks. 2016 IEEE 24th International Conference on Network Protocols.
Abstract: Information can be disseminated widely and rapidly through Online Social Networks (OSNs) with "word-of-mouth" effects. Viral marketing is such a typical application in which new products or commercial activities are advertised by some seed users in OSNs to other users in a cascading manner. The budget allocation for seed selection reflects a tradeoff between the expense and reward of viral marketing. In this paper, we define a general profit metric that naturally combines the benefit of influence spread with the cost of seed selection in viral marketing to eliminate the need for presetting the budget for seed selection. We carry out a comprehensive study on finding a set of seed nodes to maximize the profit of viral marketing. We show that the profit metric is significantly different from the influence metric in that it is no longer monotone. As a result, from the computability perspective, the problem of profit maximization is much more challenging than that of influence maximization. We develop new seed selection algorithms for profit maximization with strong approximation guarantees. Experimental evaluations with real OSN datasets demonstrate the effectiveness of our algorithms.
URI: https://hdl.handle.net/10356/81971
http://hdl.handle.net/10220/41941
DOI: 10.1109/ICNP.2016.7784445
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ICNP.2016.7784445].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers
IGS Conference Papers
SCSE Conference Papers

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