Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On the submodularity of influence in social networks
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Optimal marketing strategies over social networks
Proceedings of the 17th international conference on World Wide Web
Network externalities and the deployment of security features and protocols in the internet
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
A local mean field analysis of security investments in networks
Proceedings of the 3rd international workshop on Economics of networked systems
Diffusion of Innovations on Random Networks: Understanding the Chasm
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Efficient control of epidemics over random networks
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Pricing Strategies for Viral Marketing on Social Networks
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Kronecker Graphs: An Approach to Modeling Networks
The Journal of Machine Learning Research
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We consider the following advertisement problem in online social networks (OSNs). Given a fixed advertisement investment, e.g., a number of free samples that can be given away to a small number of users, a company needs to determine the probability that users in the OSN will eventually purchase the product. In this paper, we model OSNs as scale-free graphs (either with or without high clustering coefficient). We employ various influence mechanisms that govern the influence spreading in such large-scale OSNs and use the local mean field (LMF) technique to analyze these online social networks wherein states of nodes can be changed by various influence mechanisms. We extend our model for advertising with multiple rating levels. Extensive simulations are carried out to validate our models, which can provide insight on designing efficient advertising strategies in online social networks.