Book Chapter10.1007/978-981-19-2069-1_16
User Profiling and Influence Maximization
01 Jan 2022
- pp 221-232
TL;DR: In this article , a literature review on influence maximization topic with user profiling is presented, and the objective of this research is to present the influence maximizing, to provide a global vision of IM over big data era, and demonstrate how to identify influencers from social networks with an application.
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Abstract: AbstractFor nearly a half-century, influence maximization has been a popular topic in computational social network analysis. However, identifying k nodes “users” among all the nodes in a directed network in such a way that activating them results in the highest predicted number of activated nodes is a problem known as influence maximization. Its actual importance appears in various fields, such as targeted advertising, viral marketing, personalized recommendation, and so on. For many years, the subject of influence maximization (IM) has been addressed, and various solutions have been presented. Many social strategies assist marketers in profiling a small and specialized set of consumers in order to advertise their products and increase their influence spread, this group is called influencers. This study could be viewed as a literature review on influence maximization topic with user profiling. The objective of this research is to present the influence maximizing, to provide a global vision of IM over big data era, and demonstrate how to identify influencers from social networks with an application.KeywordsData profilingInfluence maximizationInfluencerUser profilingInformation diffusionSocial mediaPageRank algorithm
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References
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David Kempe,Jon Kleinberg,Éva Tardos +2 more
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TL;DR: An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.
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Pedro Domingos,Matthew Richardson +1 more
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