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 …

Influence maximization (IM) is the process of choosing a set of seeds from a social network
so that the most individuals will be influenced by them. Calculating the social effect of a …

As a special case of Social Influence Maximization (SIM) problems, the top k-influencers
problem aims to identify the top k actors, called members of the seed set, in a network whose …

Online social networks have become an important platform for people to communicate,
share knowledge and disseminate information. Given the widespread usage of social …

Influence maximization is among the most fundamental algorithmic problems in social
influence analysis. Over the last decade, a great effort has been devoted to developing …

We formulate and study Constrained Influence Maximization problem where a network has
two types of nodes-targets and non-targets. Given k and θ, the objective is to find a k-size …

The top k-influencers problem, as a social influence maximization (SIM) problem, seeks out
the best k actors, called the seed set, in a network with the greatest expected Influence …

We formulate and study the problem of identifying nodes whose absence can maximally
disrupt network-diffusion under the independent cascade model. We refer to such nodes as …

In the last two decades we witnessed the advent and the rapid growth of online social
networks (OSNs). The impact of their pervasive diffusion on everyday life has been dramatic …

Influence Maximization (IM) is a combinatorial optimization problem which, given a graph,
diffusion model and a number k, selects the k vertices that produce the highest combined …