… graph neural networks with actor-critic reinforcement learning. As graph neural networks apply
the same network … This makes them ideal candidates to be used as networks in semantic …

… Similarly, graph neural networks (GNNs) have also demonstrated their superior … in supervised
learning for graphstructured data. In recent times, the fusion of GNN with DRL for graph-…

… Graph neural networks (GNNs) have recently emerged as revolutionary technologies for
machine learning tasks on graphs. … To explain a graph label, we design a seed locator to learn …

… suited to learn from information structured as graphs. In this paper, we integrate Graph Neural
Networks (GNN) … GNNs are Deep Learning models inherently designed to generalize over …

… data has imposed significant challenges on the existing machine learning algorithms. …
overview of graph neural networks (GNNs) in data mining and machine learning fields. We …

… graphs. Our architecture prioritizes interventions on a temporal multi-graph by leveraging deep
Graph Neural Networks (… (3) A set of benchmarks and strong baselines, including network-…

… gRaph neuRal nETworks), to utilize the generalization capabilities of Graph Neural Networks
(GNNs) to facilitate efficient and effective multi-source policy transfer learning in the state-…

… a new problem of graph neural architecture search with reinforcement learning. We present
a GraphNAS algorithm that can design accurate graph neural network architectures that rival …

… We focus on reinforcement learning (RL) in relational problems that are naturally defined
in … We present a deep RL framework based on graph neural networks and auto-regressive …

… Therefore, we present a novel deep reinforcement learning-based … graphic convolution
neural network with deep Q-network to form an innovative graphic convolution Q network that …