New trends in influence maximization models
The growing popularity of social networks is providing a promising opportunity for different
practical applications. The influence analysis is an essential technique supporting the …
practical applications. The influence analysis is an essential technique supporting the …
[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 …
Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic
This research proposes a new type of Grey Wolf optimizer named Gradient-based Grey Wolf
Optimizer (GGWO). Using gradient information, we accelerated the convergence of the …
Optimizer (GGWO). Using gradient information, we accelerated the convergence of the …
Identification of influential users in social network using gray wolf optimization algorithm
A challenging issue in viral marketing is to effectively identify a set of influential users. By
sending the advertising messages to this set, one can reach out the largest area of the …
sending the advertising messages to this set, one can reach out the largest area of the …
[HTML][HTML] Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec
S Khalilpourazari, H Hashemi Doulabi - Annals of Operations Research, 2022 - Springer
Abstract World Health Organization (WHO) stated COVID-19 as a pandemic in March 2020.
Since then, 26,795,847 cases have been reported worldwide, and 878,963 lost their lives …
Since then, 26,795,847 cases have been reported worldwide, and 878,963 lost their lives …
A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks
Influence maximization problem aims to select a subset of k most influential nodes from a
given network such that the spread of influence triggered by the seed set will be maximum …
given network such that the spread of influence triggered by the seed set will be maximum …
Identifying influential spreaders in social networks through discrete moth-flame optimization
Influence maximization in a social network refers to the selection of node sets that support
the fastest and broadest propagation of information under a chosen transmission model. The …
the fastest and broadest propagation of information under a chosen transmission model. The …
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 …
Influence maximization in complex networks by using evolutionary deep reinforcement learning
Influence maximization (IM) in complex networks tries to activate a small subset of seed
nodes that could maximize the propagation of influence. The studies on IM have attracted …
nodes that could maximize the propagation of influence. The studies on IM have attracted …