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Finding ways to maximize influence on social networks is a significant endeavour for a wide range of people including those involved in marketing, election campaigns, and outbreak detection, for instance. Technically in a network scenario, "Influence maximisation deals with the problem of finding a subset of nodes called seeds in the social network such that these nodes will eventually spread maximum influence in the network."
Writing in the International Journal of Computational Science and Engineering researchers from India point out that this is one of a class of difficult-to-solve problems known as NP-hard problems. In their paper, they focus on providing an overview of the influence maximisation problem and cover three major aspects. First, they look at the different types of inputs required. Secondly, they investigate influence propagation models that map the spread of influence in a network. Finally, they look at approximation algorithms proposed for seed set selection.
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