Abstract
Influence maximization is a NP-hard problem which involves finding k seed nodes among all the nodes in a directed network, such that activating them leads to the maximum expected number of activated nodes. In this chapter, we will learn a \(63\%\)-optimal approximate solution. The chapter will also look at viral marketing strategies where a customer’s value is not only limited by her intrinsic value but also includes her network value. Studies of these ideas on EachMovie and Epinions will also be covered. We will briefly look at the CELF and SIMPATH algorithms.
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Raj P. M., K., Mohan, A., Srinivasa, K.G. (2018). Influence Maximisation. In: Practical Social Network Analysis with Python. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-96746-2_9
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DOI: https://doi.org/10.1007/978-3-319-96746-2_9
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