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A Survey on Influence Maximization in a Social Network
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In this paper, the authors present a survey on the progress in and around the TSS problem and discuss current research trends and future research directions, as well as discuss current and future directions as well.Abstract:
Given a social network with diffusion probabilities as edge weights and an integer k, which k nodes should be chosen for initial injection of information to maximize influence in the network? This problem is known as Target Set Selection in a social network (TSS Problem) and more popularly, Social Influence Maximization Problem (SIM Problem). This is an active area of research in computational social network analysis domain since one and half decades or so. Due to its practical importance in various domains, such as viral marketing, target advertisement, personalized recommendation, the problem has been studied in different variants, and different solution methodologies have been proposed over the years. Hence, there is a need for an organized and comprehensive review on this topic. This paper presents a survey on the progress in and around TSS Problem. At last, it discusses current research trends and future research directions as well.read more
Citations
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An improved influence maximization method for social networks based on genetic algorithm
TL;DR: In this paper, a dynamic generalized genetic algorithm (GDGA) was used to obtain a dynamic seed set in social networks under independent cascade models to identify influential nodes in these snapshot graphs.
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Novel Influence Maximization Algorithm for Social Network Behavior Management
TL;DR: An interest based algorithm with parallel social action that enables identifying influential users in social network and offers improved efficiency in the calculation speed on real world networks is proposed.
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New trends in influence maximization models
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