arXiv subjects

Pedro Almagro-Blanco

Publications and source records attributed to Pedro Almagro-Blanco.

7 recordsLinked to original sources

arXiv2026

Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization

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 for even larger graphs. This raises a central question: when can a model trained on a smaller, scaled-down replica of a graph be deployed on the full-resolution graph without retraining? We introduce a zero-shot transfer protocol in which a GNN is trained on a graph coarse-grained by geometric renormalization (GR), and the resulting weights are transferred directly to the original network. Across synthetic and real-world networks, training on GR scaled-down replicas preserves much of the original-scale predictive performance while significantly reducing training cost. We further find that learned representations and predictive trajectories remain aligned across scales. These findings suggest that structural similarity may be more important than network size in determining GNN transferability, opening a path toward scale-equivariant graph architectures.

cs.LG↗

arXiv2021

Detecting the ultra low dimensionality of real networks

Reducing dimension redundancy to find simplifying patterns in high-dimensional datasets and complex networks has become a major endeavor in many scientific fields. However, detecting the dimensionality of their latent space is challenging but necessary to generate efficient embeddings to be used in a multitude of downstream tasks. Here, we propose a method to infer the dimensionality of networks without the need for any a priori spatial embedding. Due to the ability of hyperbolic geometry to capture the complex connectivity of real networks, we detect ultra low dimensionality far below values reported using other approaches. We applied our method to real networks from different domains and found unexpected regularities, including: tissue-specific biomolecular networks being extremely low dimensional; brain connectomes being close to the three dimensions of their anatomical embedding; and social networks and the Internet requiring slightly higher dimensionality. Beyond paving the way towards an ultra efficient dimensional reduction, our findings help address fundamental issues that hinge on dimensionality, such as universality in critical behavior.

physics.soc-ph↗

arXiv2019

Small worlds and clustering in spatial networks

Networks with underlying metric spaces attract increasing research attention in network science, statistical physics, applied mathematics, computer science, sociology, and other fields. This attention is further amplified by the current surge of activity in graph embedding. In the vast realm of spatial network models, only a few reproduce even the most basic properties of real-world networks. Here, we focus on three such properties--sparsity, small worldness, and clustering--and identify the general subclass of spatial homogeneous and heterogeneous network models that are sparse small worlds and that have nonzero clustering in the thermodynamic limit. We rely on the maximum entropy approach where network links correspond to noninteracting fermions whose energy dependence on spatial distances determines network small worldness and clustering.

physics.soc-ph↗

arXiv2019

Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling

Network embedding techniques inspired by word2vec represent an effective unsupervised relational learning model. Commonly, by means of a Skip-Gram procedure, these techniques learn low dimensional vector representations of the nodes in a graph by sampling node-context examples. Although many ways of sampling the context of a node have been proposed, the effects of the way a node is chosen have not been analyzed in depth. To fill this gap, we have re-implemented the main four word2vec inspired graph embedding techniques under the same framework and analyzed how different sampling distributions affects embeddings performance when tested in node classification problems. We present a set of experiments on different well known real data sets that show how the use of popular centrality distributions in sampling leads to improvements, obtaining speeds of up to 2 times in learning times and increasing accuracy in all cases.

cs.LG↗

arXiv2017

Semantic Preserving Embeddings for Generalized Graphs

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 semantic characteristics of the original data can be obtained from a given graph using neural encoding architectures and considering the topological properties of the graph. Semantic features of these new representations are tested by using some machine learning tasks and new directions on efficient link discovery, entitity retrieval and long distance query methodologies on large relational datasets are investigated using real datasets. ---- En este trabajo se presenta un nuevo enfoque en el contexto del aprendizaje automático multi-relacional para el estudio de Grafos Generalizados. Se muestra cómo se pueden obtener representaciones vectoriales que mantienen características semánticas del grafo original utilizando codificadores neuronales y considerando las propiedades topológicas del grafo. Además, se evalúan las características semánticas capturadas por estas nuevas representaciones y se investigan nuevas metodologías eficientes relacionadas con Link Discovery, Entity Retrieval y consultas a larga distancia en grandes conjuntos de datos relacionales haciendo uso de bases de datos reales.

cs.AI↗

arXiv2017

Induction of Decision Trees based on Generalized Graph Queries

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 can provide valuable information for the learning task. In this paper we present GGQ-ID3, a multi-relational decision tree learning algorithm that uses Generalized Graph Queries (GGQ) as predicates in the decision nodes. GGQs allow to express complex patterns (including cycles) and they can be refined step-by-step. Also, they can evaluate structures (not only single records) and perform Regular Pattern Matching. GGQ are built dynamically (pattern mining) during the GGQ-ID3 tree construction process. We will show how to use GGQ-ID3 to perform multi-relational machine learning keeping complexity under control. Finally, some real examples of automatically obtained classification trees and semantic patterns are shown. ----- Normalmente, los algoritmos de inducción de árboles de decisión trabajan con datos no relacionales. Dado un registro, no tienen en cuenta los atributos de otros objetos a pesar de que éstos pueden proporcionar información útil para la tarea de aprendizaje. En este artículo presentamos GGQ-ID3, un algoritmo de aprendizaje de árboles de decisiones multi-relacional que utiliza Generalized Graph Queries (GGQ) como predicados en los nodos de decisión. Los GGQs permiten expresar patrones complejos (incluyendo ciclos) y pueden ser refinados paso a paso. Además, pueden evaluar estructuras (no solo registros) y llevar a cabo Regular Pattern Matching. En GGQ-ID3, los GGQ son construidos dinámicamente (pattern mining) durante el proceso de construcción del árbol. Además, se muestran algunos ejemplos reales de árboles de clasificación multi-relacionales y patrones semánticos obtenidos automáticamente.

cs.LG↗

arXiv2017

Generalized Graph Pattern Matching

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 into account other objects even though they are directly connected to it and can provide valuable information for the learning task. In this paper we present the concept of Generalized Graph Query, a query tool over graphs or multi-relational data structures. They are built using the same graph structure as generalized graphs and allow to express powerful relational and non-relational restrictions on this type of data. Also, this paper shows mechanisms to build this kind of queries dynamically and how they can be used to perform bottom-up discovery processes through machine laerning techniques. ----- La mayoría de los algoritmos que aprenden a partir de datos están limitados ya que sólo son capaces de aprender a partir de datos estructurados en forma de tabla en la que cada fila representa un registro y cada columna una propiedad asociada. Estos algoritmos, no tienen en cuenta los atributos de las estructuras con las que un registro dado puede estar relacionado, a pesar de que éstos pueden aportar información útil a la hora de llevar a cabo la tarea de aprendizaje. En este artículo presentamos el concepto de Generalized Graph Query, una herramienta de consulta de patrones en grafos generalizados. Dicha herramienta ha sido construida utilizando la estructura de Grafo Generalizado y permite expresar restricciones relacionales y no relacionales sobre este tipo de estructuras. Además, en este artículo se presentan mecanismos para la construcción automática de este tipo de consultas y se muestra cómo éstas pueden ser utilizadas en procesos de descubrimiento tipo bottom-up a través de técnicas relacionadas con el Aprendizaje Automático.

cs.DB↗