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 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 …
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 …
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 …
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 …
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 …
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 …
Boosting reinforcement learning in competitive influence maximization with transfer learning
Companies aim to promote their products under competitions and try to gain more profit than
other companies. This problem is formulated as a Competitive Influence Maximization (CIM) …
other companies. This problem is formulated as a Competitive Influence Maximization (CIM) …
Learning heuristics over large graphs via deep reinforcement learning
There has been an increased interest in discovering heuristics for combinatorial problems
on graphs through machine learning. While existing techniques have primarily focused on …
on graphs through machine learning. While existing techniques have primarily focused on …
Deep graph representation learning and optimization for influence maximization
Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …
network to maximize the expected number of influenced users. Researchers have made …