31 citations · 32 across the 4 of their papers we have counts for
4 papers
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…
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,…
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…
Reconstructing firm-level interactions: the Dutch input-output network
Leonardo Niccolò Ialongo, Camille de Valk, Emiliano Marchese +4
Recent crises have shown that the knowledge of the structure of input-output networks at the firm level is crucial when studying economic resilience from the microscopic point of v…