8 citations · 8 across the 4 of their papers we have counts for
5 papers
Hyperbolic Graph Representation Learning: Embed in One Metric, Optimize with Another
Federico Larroca, Paola Bermolen, Marcelo Fiori +1
Hierarchical graphs embed in hyperbolic space with lower distortion than in Euclidean space owing to its negative curvature. However, their gradient-based learning is hampered at l…
A Unified Framework of Hyperbolic Graph Representation Learning Methods
Sofía Pérez Casulo, Marcelo Fiori, Bernardo Marenco +1
Hyperbolic geometry has emerged as an effective latent space for representing complex networks, owing to its ability to capture hierarchical organization and heterogeneous connecti…
Weighted Random Dot Product Graphs
Bernardo Marenco, Paola Bermolen, Marcelo Fiori +2
Modeling of intricate relational patterns has become a cornerstone of contemporary statistical research and related data science fields. Networks, represented as graphs, offer a na…
Gradient-Based Spectral Embeddings of Random Dot Product Graphs
Marcelo Fiori, Bernardo Marenco, Federico Larroca +2
The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially po…
Online Change Point Detection for Weighted and Directed Random Dot Product Graphs
Bernardo Marenco, Paola Bermolen, Marcelo Fiori +2
Given a sequence of random (directed and weighted) graphs, we address the problem of online monitoring and detection of changes in the underlying data distribution. Our idea is to…