Revisiting non-progressive influence models: Scalable influence maximization in social networks

Golshan Golnari, Amir T. Asiaee, Arindam Banerjee, Zhi Li Zhang

Research output: Contribution to conferencePaperpeer-review

Abstract

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 realworld interpretations. We show that HC unifies, generalizes, and extends the existing nonprogressive models, such as the Voter model.

Original languageEnglish (US)
Pages316-325
Number of pages10
StatePublished - 2015
Externally publishedYes
Event31st Conference on Uncertainty in Artificial Intelligence, UAI 2015 - Amsterdam, Netherlands
Duration: Jul 12 2015Jul 16 2015

Other

Other31st Conference on Uncertainty in Artificial Intelligence, UAI 2015
Country/TerritoryNetherlands
CityAmsterdam
Period7/12/157/16/15

ASJC Scopus subject areas

  • Artificial Intelligence

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