Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
Graph representation learning for popularity prediction problem: a survey
The online social platforms, like Twitter, Facebook, LinkedIn and WeChat, have grown really
fast in last decade and have been one of the most effective platforms for people to …
fast in last decade and have been one of the most effective platforms for people to …
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 …
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 …
BatchedGreedy: A batch processing approach for influence maximization with candidate constraint
X Han, X Yao, H Huang - Applied Intelligence, 2023 - Springer
Influence maximization (IM) aims to find k seed nodes from social network G to maximize the
spread of influence under a given diffusion model. However, in real social marketing …
spread of influence under a given diffusion model. However, in real social marketing …
Never Explore Repeatedly in Multi-Agent Reinforcement Learning
In the realm of multi-agent reinforcement learning, intrinsic motivations have emerged as a
pivotal tool for exploration. While the computation of many intrinsic rewards relies on …
pivotal tool for exploration. While the computation of many intrinsic rewards relies on …
Dyadic Reinforcement Learning
Mobile health aims to enhance health outcomes by delivering interventions to individuals as
they go about their daily life. The involvement of care partners and social support networks …
they go about their daily life. The involvement of care partners and social support networks …
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization
Influence maximization (IM) is a combinatorial problem of identifying a subset of nodes
called the seed nodes in a network (graph), which when activated, provide a maximal …
called the seed nodes in a network (graph), which when activated, provide a maximal …
[PDF][PDF] A Learning Approach to Complex Contagion Influence Maximization
Influence maximization (IM) aims to find a set of seed nodes in a social network that
maximizes the influence spread. While most IM problems focus on classical influence …
maximizes the influence spread. While most IM problems focus on classical influence …
[PDF][PDF] Complex Contagion Influence Maximization: A Reinforcement Learning Approach
In influence maximization (IM), the goal is to find a set of seed nodes in a social network that
maximizes the influence spread. While most IM problems focus on classical influence …
maximizes the influence spread. While most IM problems focus on classical influence …