4 papers
Multi-Scale Node Embeddings for Graph Modeling and Generation
Riccardo Milocco, Fabian Jansen, Diego Garlaschelli
Lying at the interface between Network Science and Machine Learning, node embedding algorithms take a graph as input and encode its structure onto output vectors that represent nod…
Renormalizable Graph Embeddings For Multi-Scale Network Reconstruction
Riccardo Milocco, Fabian Jansen, Diego Garlaschelli
In machine learning, graph embedding algorithms seek low-dimensional representations of the input network data, thereby allowing for downstream tasks on compressed encodings. Recen…
Quantum generative modeling for financial time series with temporal correlations
David Dechant, Eliot Schwander, Lucas van Drooge +4
Quantum generative adversarial networks (QGANs) have been investigated as a method for generating synthetic data with the goal of augmenting training data sets for neural networks.…
Multi-scale reconstruction of large supply networks
Leonardo Niccolò Ialongo, Sylvain Bangma, Fabian Jansen +1
The structure of the supply chain network has important implications for modelling economic systems, from growth trajectories to responses to shocks or natural disasters. However,…