[HTML][HTML] Influence maximization in social networks: Theories, methods and challenges
Y Ye, Y Chen, W Han - Array, 2022 - Elsevier
Influence maximization (IM) is the process of choosing a set of seeds from a social network
so that the most individuals will be influenced by them. Calculating the social effect of a …
so that the most individuals will be influenced by them. Calculating the social effect of a …
FIP: A fast overlapping community-based Influence Maximization Algorithm using probability coefficient of global diffusion in social networks
Influence maximization is the process of identifying a small set of influential nodes from a
complex network to maximize the number of activation nodes. Due to the critical issues such …
complex network to maximize the number of activation nodes. Due to the critical issues such …
Determination of influential nodes based on the Communities' structure to maximize influence in social networks
With the increasing development of social networks, they have turned into important
research platforms. Influence maximization is one of the most important research issues in …
research platforms. Influence maximization is one of the most important research issues in …
A survey on meta-heuristic algorithms for the influence maximization problem in the social networks
The different communications of users in social networks play a key role in effect to each
other. The effect is important when they can achieve their goals through different …
other. The effect is important when they can achieve their goals through different …
Influence maximization in social networks using effective community detection
Influence maximization problem aims to find a set of nodes with the highest diffusion in
social networks in order to maximize diffusion in the graph by this set. A set of these nodes …
social networks in order to maximize diffusion in the graph by this set. A set of these nodes …
Influence maximization problem by leveraging the local traveling and node labeling method for discovering most influential nodes in social networks
The influence maximization problem has gained particular importance in viral marketing for
large-scale spreading in social networks. Developing a fast and appropriate algorithm to …
large-scale spreading in social networks. Developing a fast and appropriate algorithm to …
Influence maximization in social networks based on discrete harris hawks optimization algorithm
C Fan, Z Wang, J Zhang, J Zhao, X Rui - Computing, 2024 - Springer
Influence Maximization (IM) is an important topic in the field of social network analysis, and
is widely used in viral marketing, recommendation systems, rumor prevention and other …
is widely used in viral marketing, recommendation systems, rumor prevention and other …
Optimal selection of seed nodes by reducing the influence of common nodes in the influence maximization problem
F Kazemzadeh, AA Safaei… - 2022 13th International …, 2022 - ieeexplore.ieee.org
The Internet and social networks play an important role in today's world. Many large
companies and organizations use the Internet to manage and implement their activities …
companies and organizations use the Internet to manage and implement their activities …
A node filtering approach for Influence Maximization problem in Independent Cascade model
Identify influential nodes is essential to the immunization and propagation process on
complex networks that this node selects based on the influence maximization problem …
complex networks that this node selects based on the influence maximization problem …
Identifying top-k influential nodes in social networks: a discrete hybrid optimizer by integrating butterfly optimization algorithm with differential evolution
J Tang, H Zhu, L Han, S Song - The Journal of Supercomputing, 2024 - Springer
The most challenge of influence maximization (IM) is to locate a finite set of influencers while
maximizing the influence dissemination in a social network. Due to the increasingly …
maximizing the influence dissemination in a social network. Due to the increasingly …