Abstract Information diffusion is a hot research topic in the social computing field, which has
a wide range of applications in economy, politics, and life. Most of the existing studies mainly …

Influence maximization (IM) is one of the most important problems in social network analysis.
Its objective is to find a given number of seed nodes that maximize the spread of information …

Selecting a set of initial users from a social network in order to maximize the envisaged
number of influenced users is known as influence maximization (IM). Researchers have …

With the rapid development of edge computing, online social network services are growing
explosively. Social influence plays a critical role in the propagation of social network …

Recent years, many online network communities, such as Facebook, Twitter, Tik Tok, Weibo,
etc., have developed rapidly and become the bridge connecting physical social world and …

Abstract Influence Maximization (IM), an NP combinatorial optimization problem, has been
broadly studied in the past decades. Existing algorithms for IM are still limited by accuracy …

Influence maximization (IM) is one of the most important problems in social network analysis.
Its objective is to find a given number of seed nodes that maximize the spread of information …

The problem of maximizing influence aims to select a portion of seed users in social
networks, making the diffusion wider of users' opinions. For a topic, the diffusion width is the …

In recent two decades, online social platforms have become more and more popular, and
the dissemination of information on social networks has attracted wide attention of the …

Influencing customers through social media is a new form of marketing. Recently, there were
studies on the Influence Maximization (IM) problem, which aimed to identify influencers that …