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 …
ToupleGDD: A fine-designed solution of influence maximization by deep reinforcement learning
Aiming at selecting a small subset of nodes with maximum influence on networks, the
influence maximization (IM) problem has been extensively studied. Since it is# P-hard to …
influence maximization (IM) problem has been extensively studied. Since it is# P-hard to …
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 …
LAIM: A linear time iterative approach for efficient influence maximization in large-scale networks
H Wu, J Shang, S Zhou, Y Feng, B Qiang, W Xie - IEEE Access, 2018 - ieeexplore.ieee.org
The problem of influence maximization (IM) has been extensively studied in recent years
and has many practical applications such as social advertising and viral marketing. Given …
and has many practical applications such as social advertising and viral marketing. Given …
Piano: Influence maximization meets deep reinforcement learning
H Li, M Xu, SS Bhowmick, JS Rayhan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Since its introduction in 2003, the influence maximization (IM) problem has drawn significant
research attention in the literature. The aim of IM, which is NP-hard, is to select a set of users …
research attention in the literature. The aim of IM, which is NP-hard, is to select a set of users …
Disco: Influence maximization meets network embedding and deep learning
Since its introduction in 2003, the influence maximization (IM) problem has drawn significant
research attention in the literature. The aim of IM is to select a set of k users who can …
research attention in the literature. The aim of IM is to select a set of k users who can …
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 …
Leveraging transfer learning in reinforcement learning to tackle competitive influence maximization
Competitive influence maximization (CIM) is a key problem that seeks highly influential
users to maximize the party's reward than the competitor. Heuristic and game theory-based …
users to maximize the party's reward than the competitor. Heuristic and game theory-based …
Addressing competitive influence maximization on unknown social network with deep reinforcement learning
Recent studies have considered the reinforcement and deep reinforcement learning models
to address the competitive influence maximization (CIM) problem. However, these models …
to address the competitive influence maximization (CIM) problem. However, these models …
Network dynamic GCN influence maximization algorithm with leader fake labeling mechanism
C Zhang, W Li, D Wei, Y Liu, Z Li - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Influence maximization is an important technique for its significant value on various social
network applications, such as viral marketing, advertisement, and recommendation …
network applications, such as viral marketing, advertisement, and recommendation …