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 …

Information diffusion in social networks is emerging as a promising solution to successful
viral marketing, which relies on the effective and efficient identification of a set of nodes with …

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) 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 …

Influence maximization, defined by Kempe et al.(SIGKDD 2003), is the problem of finding a
small set of seed nodes in a social network that maximizes the spread of influence under …

Influence maximization, defined as finding a small subset of nodes that maximizes spread of
influence in social networks, is NP-hard under both Independent Cascade (IC) and Linear …

We consider stochastic influence maximization problems arising in social networks. In
contrast to existing studies that involve greedy approximation algorithms with a 63 …

Influence maximization is the problem of finding a set of users in a social network, such that
by targeting this set, one maximizes the expected spread of influence in the network. Most of …

Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a
social network that could maximize the spread of influence. In this paper, we study the …

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be
widely used in mobile networks, social computing, and recommendation systems. It aims at …