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 …
LIDDE: A differential evolution algorithm based on local-influence-descending search strategy for influence maximization in social networks
L Qiu, X Tian, J Zhang, C Gu, S Sai - Journal of Network and Computer …, 2021 - Elsevier
Influence maximization aims to select k seed nodes from social networks so that the
expected number of nodes activated by the seed nodes can be maximized. With the …
expected number of nodes activated by the seed nodes can be maximized. With the …
Influence maximization in social networks based on discrete particle swarm optimization
Influence maximization in social networks aims to find a small group of individuals, which
have maximal influence cascades. In this study, an optimization model based on a local …
have maximal influence cascades. In this study, an optimization model based on a local …
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 …
LAPSO-IM: A learning-based influence maximization approach for social networks
Online social networks play a pivotal role in the propagation of information and influence as
in the form of word-of-mouth spreading. Influence maximization (IM) is a fundamental …
in the form of word-of-mouth spreading. Influence maximization (IM) is a fundamental …
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 …
DDSE: A novel evolutionary algorithm based on degree-descending search strategy for influence maximization in social networks
Influence maximization (IM) is the problem of finding a small subset of nodes in a social
network so that the number of nodes influenced by this subset can be maximized. Influence …
network so that the number of nodes influenced by this subset can be maximized. Influence …
TIFIM: A two-stage iterative framework for influence maximization in social networks
Influence Maximization is an important problem in social networks, and its main goal is to
select some most influential initial nodes (ie, seed nodes) to obtain the maximal influence …
select some most influential initial nodes (ie, seed nodes) to obtain the maximal influence …
An efficient and effective hop-based approach for influence maximization in social networks
Influence maximization in social networks is a classic and extensively studied problem that
targets at selecting a set of initial seed nodes to spread the influence as widely as possible …
targets at selecting a set of initial seed nodes to spread the influence as widely as possible …
Influence maximization across heterogeneous interconnected networks based on deep learning
With the fast development of online social networks, a large number of their members are
involved in more than one social network. Finding most influential users is one of the …
involved in more than one social network. Finding most influential users is one of the …