2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
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Abstract

One highly studied topic in the field of social networks is the search for influential nodes, that when seeded (i.e. infected intentionally), may infect a large portion of the network through a viral process. However, when it comes to the spread of new products, such viral processes are rather rare. Social influence is indeed an important factor when it comes to the act of adopting a new product. However, this influence is usually latent and does not trigger the purchase action by itself, it therefore requires an additional sales effort. We propose a model and a method that better fit the product adoption scenario. Our method allocates the seeding efforts not only to precise nodes but also at precise points in time, such that the product adoption rate increases. By conducting a set of empirical simulations, we show that under realistic assumptions, our method improves the product adoption rate by 25%–50%.
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