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 …

The growing popularity of social networks is providing a promising opportunity for different
practical applications. The influence analysis is an essential technique supporting the …

With the boom in technologies and mobile networks in recent years, online social networks
have become an integral part of our daily lives. These virtual networks connect people …

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 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 …

Influence maximization (IM) has shown wide applicability in immense fields over the past
decades. Previous researches on IM mainly focused on the dyadic relationship but lacked …

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 …