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Found 4 papers, 1 papers with code
Pearl: Parallel Evolutionary and Reinforcement Learning Library
Reinforcement learning is increasingly finding success across domains where the problem can be represented as a Markov decision process.
Graph Theory for Metro Traffic Modelling
Finally, we also explore the application of modern deep learning models, such as graph neural networks and hyper-graph neural networks, as general purpose models for the modelling and forecasting of underground data, especially in the context of the morning and evening rush hours.
Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications
Many modern data analytics applications on graphs operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition, rather than serving as prior knowledge which aids the problem solution.
Portfolio Cuts: A Graph-Theoretic Framework to Diversification
Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure.
