Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …

Influence maximization is an important research area in social network analysis where
researchers are concerned with detecting influential nodes. The detection of influential …

Aiming at selecting a small subset of nodes with maximum influence on networks, the
influence maximization (IM) problem has been extensively studied. Since it is# P-hard to …

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 …

Balanced influence maximization aims to balance the influence maximization of multiple
different entities in social networks and avoid the emergence of filter bubbles and echo …

In the recent era, the communication network with the support of many wireless technologies
is giving benefits for remote access. Such a communication model increases the flexibility for …

A misinformation prevention problem is essential in social networks since misinformation
could greatly mislead people and interfere societal, economical, or even political …

Influence maximization is a crucial issue for mining the deep information of social networks,
which aims to select a seed set from the network to maximize the number of influenced …

Currently, information diffusion on online platforms plays an important role in social
governance. Opinions contained in information have a significant influence on information …

Identifying an optimal set of nodes that can maximize the spread of influence in a network is
a crucial challenge in network science. It has numerous applications such as epidemic …