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 …

Given a social network G and a constant k, the influence maximization problem asks for k
nodes in G that (directly and indirectly) influence the largest number of nodes under a pre …

Influence Maximization (IM), that seeks a small set of key users who spread the influence
widely into the network, is a core problem in multiple domains. It finds applications in viral …

Social influence analysis (SIA) is a vast research field that has attracted research interest in
many areas. In this paper, we present a survey of representative and state-of-the-art work in …

Online social networks play a major role not only in socio psychological front, but also in the
economic aspect. The way social network serves as a platform of information spread, has …

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 …

Online social networks have been one of the most effective platforms for marketing and
advertising. Through the “world-of-mouth” exchanges, so-called viral marketing, 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 …

Influence maximization (IM), which selects a set of $ k $ users (called seeds) to maximize the
influence spread over a social network, is a fundamental problem in a wide range of …

Online social networks have been one of the most effective platforms for marketing and
advertising. Through the “world-of-mouth” exchanges, so-called viral marketing, the …