… This makes it hard to learn abstract representations of mutual … graph convolutional
reinforcement learning, where graph convolution adapts to the dynamics of the underlying graph of …

… learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a
novel reinforcement learning framework for learn… based on knowledge graph embeddings, …

… 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-…

… on graphstructured environments. We show the times cited and publications over time of Graph
Reinforcement Learning … 1 to prove the fusion of graph mining methods with RL for graph-…

… study the problem of using reinforcement learning to search graph neural architectures,
which has the potential to save a lot of manual work for designing graph neural architectures. …

… general framework for learning how to control such dynamic processes on graphs. … graphs.
Our architecture prioritizes interventions on a temporal multi-graph by leveraging deep Graph …

… To explain a graph label, we design a seed locator to learn the node that influences the
graph label the most. Iterative graph generation is the key module in our method, which …

… Temporal knowledge graph (TKG) reasoning is a crucial task that has gained … reinforcement
learning method for forecasting. Specifically, the agent travels on historical knowledge graph …

… a graph mask, leveraging our knowledge graph at that timestep Gt to streamline the object
decoding process. Formally, the graph … found within the knowledge graph Gt and vocabulary V …

… , we introduce Graph Pointer Networks (GPNs) trained using reinforcement learning (RL) for
… GPNs build upon Pointer Networks by introducing a graph embedding layer on the input, …