1 code implementation • 27 Nov 2020 • Matheus R. F. Mendonça, André M. S. Barreto, Artur Ziviani
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.
1 code implementation • 29 Jun 2020 • Matheus R. F. Mendonça, André M. S. Barreto, Artur Ziviani
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.
no code implementations • 21 Jul 2014 • André M. S. Barreto, Doina Precup, Joelle Pineau
In this paper we introduce an algorithm that turns KBRL into a practical reinforcement learning tool.
no code implementations • 2 Jul 2014 • Amir-Massoud Farahmand, Doina Precup, André M. S. Barreto, Mohammad Ghavamzadeh
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.