39 citations · 81 across the 7 of their papers we have counts for
7 papers
Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization
Robert Jankowski, Pedro Almagro-Blanco, Marián Boguñá +2
Graph neural networks (GNNs) can operate on large graphs but become infrastructure-sensitive at the scale of millions of nodes and typically require scalable training techniques fo…
Detecting the ultra low dimensionality of real networks
Pedro Almagro-Blanco, Marian Boguna, M. Angeles Serrano
Reducing dimension redundancy to find simplifying patterns in high-dimensional datasets and complex networks has become a major endeavor in many scientific fields. However, detecti…
Small worlds and clustering in spatial networks
Marian Boguna, Dmitri Krioukov, Pedro Almagro-Blanco +1
Networks with underlying metric spaces attract increasing research attention in network science, statistical physics, applied mathematics, computer science, sociology, and other fi…
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling
Pedro Almagro-Blanco, Fernando Sancho-Caparrini
Network embedding techniques inspired by word2vec represent an effective unsupervised relational learning model. Commonly, by means of a Skip-Gram procedure, these techniques learn…
Semantic Preserving Embeddings for Generalized Graphs
Pedro Almagro-Blanco, Fernando Sancho-Caparrini
A new approach to the study of Generalized Graphs as semantic data structures using machine learning techniques is presented. We show how vector representations maintaining semanti…
Induction of Decision Trees based on Generalized Graph Queries
Pedro Almagro-Blanco, Fernando Sancho-Caparrini
Usually, decision tree induction algorithms are limited to work with non relational data. Given a record, they do not take into account other objects attributes even though they ca…
Generalized Graph Pattern Matching
Pedro Almagro-Blanco, Fernando Sancho-Caparrini
Most of the machine learning algorithms are limited to learn from flat data: a recordset with prefixed structure. When learning from a record, these types of algorithms don't take…