[HTML][HTML] Influence maximization frameworks, performance, challenges and directions on social network: A theoretical study
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 …
network to provide solutions for real-world problems like outbreak detection, viral marketing …
Fairsna: Algorithmic fairness in social network analysis
In recent years, designing fairness-aware methods has received much attention in various
domains, including machine learning, natural language processing, and information …
domains, including machine learning, natural language processing, and information …
A two-stage VIKOR assisted multi-operator differential evolution approach for Influence Maximization in social networks
The impact of online social networking on daily life is extending beyond personal
boundaries, becoming a tool for financial activities and even public well-being. Interactions …
boundaries, becoming a tool for financial activities and even public well-being. Interactions …
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 …
An MCDM integrated adaptive simulated annealing approach for influence maximization in social networks
Abstract The Influence Maximization (IM) problem aims to identify a small subset of nodes
that have the most influence spread in a network. Although it is an NP-hard problem, the …
that have the most influence spread in a network. Although it is an NP-hard problem, the …
A survey on influence maximization: From an ml-based combinatorial optimization
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 …
widely used in mobile networks, social computing, and recommendation systems. It aims at …
[图书][B] Social data analytics
This book is an introduction to social data analytics along with its challenges and
opportunities in the age of Big Data and Artificial Intelligence. It focuses primarily on …
opportunities in the age of Big Data and Artificial Intelligence. It focuses primarily on …
[HTML][HTML] ABEM: an adaptive agent-based evolutionary approach for influence maximization in dynamic social networks
Influence maximization is recognized as a crucial optimization problem, which aims to
identify a limited set of influencers to maximize the coverage of influence dissemination in …
identify a limited set of influencers to maximize the coverage of influence dissemination in …
A centrality measure for quantifying spread on weighted, directed networks
CG Fink, K Fullin, G Gutierrez, N Omodt… - Physica A: Statistical …, 2023 - Elsevier
While many centrality measures for complex networks have been proposed, relatively few
have been developed specifically for weighted, directed (WD) networks. Here we propose a …
have been developed specifically for weighted, directed (WD) networks. Here we propose a …
Modeling the spread of infectious diseases through influence maximization
Mathematical approaches, such as compartmental models and agent-based models, have
been utilized for modeling the spread of the infectious diseases in the computational …
been utilized for modeling the spread of the infectious diseases in the computational …