Real networks exhibit heterogeneous nature with nodes playing far different roles in
structure and function. To identify vital nodes is thus very significant, allowing us to control …

Complementary to the fancy applications of social networks, influence analysis is an
indispensable technique supporting these practical applications. In recent years, this …

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 …

To meet the requirement of social influence analytics in various applications, the problem of
influence maximization has been studied in recent years. The aim is to find a limited number …

Given a social network G and a positive integer k, the influence maximization problem asks
for k nodes (in G) whose adoptions of a certain idea or product can trigger the largest …

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 …

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 …

Diffusion is a fundamental graph process, underpinning such phenomena as epidemic
disease contagion and the spread of innovation by word-of-mouth. We address the …

In the current era of automation, machines are constantly being channelized to provide
accurate interpretations of what people express on social media. The human race nowadays …