Influence Maximization (IM), which selects a set of k users (called seed set) from a social
network to maximize the expected number of influenced users (called influence spread), is a …

In this paper, we address the important issue of uncertainty in the edge influence probability
estimates for the well studied influence maximization problem---the task of finding k seed …

We study the online influence maximization problem in social networks under the
independent cascade model. Specifically, we aim to learn the set of" best influencers" in a …

In this paper we study the adaptive complexity of submodular optimization. Informally, the
adaptive complexity of a problem is the minimal number of sequential rounds required to …

Information can be disseminated widely and rapidly through Online Social Networks (OSNs)
with “word-of-mouth” effects. Viral marketing is such a typical application in which new …

Online influence maximization (OIM) is a popular problem in social networks to learn
influence propagation model parameters and maximize the influence spread at the same …

Influence functions estimate effect of individual data points on predictions of the model on
test data and were adapted to deep learning in\cite {koh2017understanding}. They have …

Starting with the earliest studies showing that the spread of new trends, information, and
innovations is closely related to the social influence exerted on people by their social …

This paper focuses on new challenges in influence maximization inspired by non-profits' use
of social networks to effect behavioral change in their target populations. Influence …

The optimal allocation of resources for maximizing influence, spread of information or
coverage, has gained attention in the past years, in particular in machine learning and data …