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Detalhes

Detalhes

  • Nome

    Pedro Manuel Ribeiro
  • Cargo

    Investigador Sénior
  • Desde

    03 maio 2010
Publicações

2024

Multilayer quantile graph for multivariate time series analysis and dimensionality reduction

Autores
Silva, VF; Silva, ME; Ribeiro, P; Silva, F;

Publicação
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS

Abstract
In recent years, there has been a surge in the prevalence of high- and multidimensional temporal data across various scientific disciplines. These datasets are characterized by their vast size and challenging potential for analysis. Such data typically exhibit serial and cross-dependency and possess high dimensionality, thereby introducing additional complexities to conventional time series analysis methods. To address these challenges, a recent and complementary approach has emerged, known as network-based analysis methods for multivariate time series. In univariate settings, quantile graphs have been employed to capture temporal transition properties and reduce data dimensionality by mapping observations to a smaller set of sample quantiles. To confront the increasingly prominent issue of high dimensionality, we propose an extension of quantile graphs into a multivariate variant, which we term Multilayer Quantile Graphs. In this innovative mapping, each time series is transformed into a quantile graph, and inter-layer connections are established to link contemporaneous quantiles of pairwise series. This enables the analysis of dynamic transitions across multiple dimensions. In this study, we demonstrate the effectiveness of this new mapping using synthetic and benchmark multivariate time series datasets. We delve into the resulting network's topological structures, extract network features, and employ these features for original dataset analysis. Furthermore, we compare our results with a recent method from the literature. The resulting multilayer network offers a significant reduction in the dimensionality of the original data while capturing serial and cross-dimensional transitions. This approach facilitates the characterization and analysis of large multivariate time series datasets through network analysis techniques.

2024

Computing Motifs in Hypergraphs

Autores
Nóbrega, D; Ribeiro, P;

Publicação
COMPLEX NETWORKS XV, COMPLENET 2024

Abstract
Motifs are overrepresented and statistically significant sub-patterns in a network, whose identification is relevant to uncover its underlying functional units. Recently, its extraction has been performed on higher-order networks, but due to the complexity arising from polyadic interactions, and the similarity with known computationally hard problems, its practical application is limited. Our main contribution is a novel approach for hyper-subgraph census and higher-order motif discovery, allowing for motifs with sizes 3 or 4 to be found efficiently, in real-world scenarios. It is consistently an order of magnitude faster than a baseline state-of-art method, while using less memory and supporting a wider range of base algorithms.

2024

Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs

Autores
Eddin, AN; Bono, J; Aparício, D; Ferreira, H; Ribeiro, P; Bizarro, P;

Publicação
CoRR

Abstract

2023

Improving the Characterization and Comparison of Football Players with Spatial Flow Motifs

Autores
Barbosa, A; Ribeiro, P; Dutra, I;

Publicação
COMPLEX NETWORKS AND THEIR APPLICATIONS XI, COMPLEX NETWORKS 2022, VOL 2

Abstract
Association Football is probably the world's most popular sport. Being able to characterise and compare football players is therefore a very important and impactful task. In this work we introduce spatial flow motifs as an extension of previous work on this problem, by incorporating both temporal and spatial information into the network analysis of football data. Our approach considers passing sequences and the role of the player in those sequences, complemented with the physical position of the field where the passes occurred. We provide experimental results of our proposed methodology on real-life event data from the Italian League, showing we can more accurately identify players when compared to using purely topological data.

2023

Towards the Concept of Spatial Network Motifs

Autores
Ferreira, J; Barbosa, A; Ribeiro, P;

Publicação
COMPLEX NETWORKS AND THEIR APPLICATIONS XI, COMPLEX NETWORKS 2022, VOL 2

Abstract
Many complex systems exist in the physical world and therefore can be modeled by networks in which their nodes and edges are embedded in space. However, classical network motifs only use purely topological information and disregard other features. In this paper we introduce a novel and general subgraph abstraction that incorporates spatial information, therefore enriching its characterization power. Moreover, we describe and implement a method to compute and count our spatial subgraphs in any given network. We also provide initial experimental results by using our methodology to produce spatial fingerprints of real road networks, showcasing its discrimination power and how it captures more than just simple topology.

Teses
supervisionadas

2023

Multidimensional Time Series Analysis: A Complex Networks Approach

Autor
Vanessa Alexandra Freitas da Silva

Instituição
UP-FCUP

2023

Anti-money Laundering using Graph Techniques

Autor
Ahmad Naser Eddin

Instituição
UP-FCUP

2023

Towards Improving the Search for Multi-Relational Concepts in ILP

Autor
Alberto José Rajão Barbosa

Instituição
UP-FCUP

2023

Propagation Patterns in Multilayer Networks

Autor
André Couto Meira

Instituição
UP-FCUP

2023

Transformers for Medical Domains

Autor
Hugo Manuel Soares Oliveira

Instituição
UP-FCUP