36 citations · 176 across the 39 of their papers we have counts for
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Adaptive Influence Maximization in Social Networks: Why Commit when You can Adapt?
Sharan Vaswani, Laks V. S. Lakshmanan
Most previous work on influence maximization in social networks is limited to the non-adaptive setting in which the marketer is supposed to select all of the seed users, to give fr…
Influence Maximization with Bandits
Sharan Vaswani, Laks. V. S. Lakshmanan, Mark Schmidt
We consider the problem of \emph{influence maximization}, the problem of maximizing the number of people that become aware of a product by finding the `best' set of `seed' users to…
Modeling Non-Progressive Phenomena for Influence Propagation
Vincent Yun Lou, Smriti Bhagat, Laks V. S. Lakshmanan +1
Recent work on modeling influence propagation focus on progressive models, i.e., once a node is influenced (active) the node stays in that state and cannot become inactive. However…