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 …

The influence maximization problem has gained increasing attention in recent years.
Previous research focuses on the development of algorithms to analyze static social …

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 …

Network embedding, which aims to learn the low-dimensional representations of vertices, is
an important task and has attracted considerable research efforts recently. In real world …

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 …

Explosive growth in the number of users on various social media platforms has transformed
the way firms strategize their marketing activities. To take advantage of the vast size of social …

Influence maximization is a classic optimization problem studied in the area of social
network analysis and viral marketing. Given a network, it is defined as the problem of finding …

Influence Maximization is an important problem in social networks, and its main goal is to
select some most influential initial nodes (ie, seed nodes) to obtain the maximal influence …

Real-world sequential data are often generated based on complicated and latent
mechanisms, which can be formulated as event sequences occurring in the continuous time …