Sampling Based Influence Maximization on Linear Threshold Model

A sampling based influence maximization on linear threshold (LT) model method is presented. The method samples the routes in the possible worlds in the social networks, and uses Chernoff bound to estimate the number of samples so that the error can be constrained within a given bound. Then the activ...

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Published in:Journal of physics. Conference series 2018-04, Vol.989 (1), p.12013
Main Authors: Jia, Su, Chen, Ling
Format: Article
Language:English
Subjects:
Maximization
Optimization
Physics
Sampling
Social networks
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description A sampling based influence maximization on linear threshold (LT) model method is presented. The method samples the routes in the possible worlds in the social networks, and uses Chernoff bound to estimate the number of samples so that the error can be constrained within a given bound. Then the active possibilities of the routes in the possible worlds are calculated, and are used to compute the influence spread of each node in the network. Our experimental results show that our method can effectively select appropriate seed nodes set that spreads larger influence than other similar methods.
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subjects Maximization
Optimization
Physics
Sampling
Social networks
title Sampling Based Influence Maximization on Linear Threshold Model
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