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 language||English (US)|
|Number of pages||10|
|State||Published - Jan 1 2015|
|Event||31st Conference on Uncertainty in Artificial Intelligence, UAI 2015 - Amsterdam, Netherlands|
Duration: Jul 12 2015 → Jul 16 2015
|Other||31st Conference on Uncertainty in Artificial Intelligence, UAI 2015|
|Period||7/12/15 → 7/16/15|