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Found 4 papers, 2 papers with code
Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learning
In this context, we propose Spatio-Temporal Influence Maximization~(STIM), a model trained with Reinforcement Learning and Graph Embedding over a set of artificial TVGs that is capable of learning the temporal behavior and connectivity pattern of each node, allowing it to predict the best moment to start a diffusion through the TVG.
Approximating Network Centrality Measures Using Node Embedding and Machine Learning
Our proposed model, entitled Network Centrality Approximation using Graph Embedding (NCA-GE), uses the adjacency matrix of a graph and a set of features for each node (here, we use only the degree) as input and computes the approximate desired centrality rank for every node.
Practical Kernel-Based Reinforcement Learning
In this paper we introduce an algorithm that turns KBRL into a practical reinforcement learning tool.
Classification-based Approximate Policy Iteration: Experiments and Extended Discussions
We introduce a general classification-based approximate policy iteration (CAPI) framework, which encompasses a large class of algorithms that can exploit regularities of both the value function and the policy space, depending on what is advantageous.
