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Revisiting Non-Progressive Influence Models: Scalable Influence Maximization
[article]
2018
arXiv
pre-print
While influence maximization in social networks has been studied extensively in computer science community for the last decade the focus has been on the progressive influence models, such as independent cascade (IC) and Linear threshold (LT) models, which cannot capture the reversibility of choices. In this paper, we present the Heat Conduction (HC) model which is a non-progressive influence model with real-world interpretations. We show that HC unifies, generalizes, and extends the existing
arXiv:1412.5718v3
fatcat:7elpym64xbh7vp4dnornzggdta