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Cost-effective viral marketing for time-critical campaigns in large-scale social networks
Dinh T., Zhang H., Nguyen D., Thai M. IEEE/ACM Transactions on Networking22 (6):2001-2011,2014.Type:Article
Date Reviewed: Nov 11 2015

To remain competitive, businesses need to maintain profitable online social networks (OSNs) for promoting different merchandise to diverse customers. How should commercial industries allocate marketing resources to online social networking products to guarantee gainful sales revenues? Dinh et al. examine this nontrivial optimization research problem.

The speedy, lucrative, and enormous viral advertising optimization problem in this paper tries to find the least number of nodes for vastly and rapidly disseminating product information over OSNs. The online social networking model is an undirected graph with vertices of users and edges of social links among users in the networks. The authors use the familiar power law theory to convincingly show that a huge number of network nodes are required to broadcast an influential product all over a network. Consequently, they propose a scalable algorithm that does not guarantee the choice of the most effective network nodes for spreading product advertisements, but allows the product information to propagate to all online social networking users.

The integer linear programming problem seeks to minimize the initial network nodes that broadcast product information, contingent upon the activation of nodes that actively spread the information among users. The authors present a SLEVA algorithm that generates optimal or suboptimal solutions to the viral advertising problem, depending on the size (users and social links) of the online social network. They use the social networks of renowned mathematicians, physicists, Facebook, and Orkut to investigate the superiority and scalability of the proposed SLEVA algorithm. At its worst, the SLEVA algorithm either performs equally to or outperforms the other algorithms in the literature. Indeed, the SLEVA algorithm is cost effective in making use of customers to influence advertisements in OSNs.

I strongly encourage all data analytics professionals to read and take advantage of the revolutionary and insightful marketing ideas in this paper.

Reviewer:  Amos Olagunju Review #: CR143929 (1601-0078)
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