The influence maximization (IM) problem identifies the subset of influential users in the
network to provide solutions for real-world problems like outbreak detection, viral marketing …

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 …

Over the recent decade, much research has been conducted in the field of social networks.
The structure of these networks has been irregular, complex, and dynamic, and certain …

Given a social network with diffusion probabilities as edge weights and a positive integer k,
which k nodes should be chosen for initial injection of information to maximize the influence …

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 …

Influence maximization is a classic and extensively studied problem with important
applications in viral marketing. Existing algorithms for influence maximization, however …

Influence maximization (IM) on social networks is one of the most active areas of research in
computer science. While various IM techniques proposed over the last decade have …

The impact of online social networking on daily life is extending beyond personal
boundaries, becoming a tool for financial activities and even public well-being. Interactions …

Derived from the idea of word-to-mouth advertising and with applying information diffusion
theory, viral marketing attracts wide research interests because of its business value. As an …

Given a social network G with n nodes and m edges, a positive integer k, and a cascade
model C, the influence maximization (IM) problem asks for k nodes in G such that the …