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Details

  • Name

    Pedro Manuel Ribeiro
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    03rd May 2010
Publications

2022

Network Science - 7th International Winter Conference, NetSci-X 2022, Porto, Portugal, February 8-11, 2022, Proceedings

Authors
Ribeiro, P; Silva, F; Ferreira Mendes, JF; Laureano, RD;

Publication
NetSci-X

Abstract

2022

Preface

Authors
Ribeiro, P; Silva, F; Mendes, JF; Laureano, R;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

2022

Novel Features for Time Series Analysis: A Complex Networks Approach

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

Publication
DATA MINING AND KNOWLEDGE DISCOVERY

Abstract

2021

Time series analysis via network science: Concepts and algorithms

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

Publication
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY

Abstract

2021

A Survey on Subgraph Counting: Concepts, Algorithms, and Applications to Network Motifs and Graphlets

Authors
Ribeiro, P; Paredes, P; Silva, MEP; Aparicio, D; Silva, F;

Publication
ACM COMPUTING SURVEYS

Abstract
Computing subgraph frequencies is a fundamental task that lies at the core of several network analysis methodologies, such as network motifs and graphlet-based metrics, which have been widely used to categorize and compare networks from multiple domains. Counting subgraphs is, however, computationally very expensive, and there has been a large body of work on efficient algorithms and strategies to make subgraph counting feasible for larger subgraphs and networks. This survey aims precisely to provide a comprehensive overview of the existing methods for subgraph counting. Our main contribution is a general and structured review of existing algorithms, classifying them on a set of key characteristics, highlighting their main similarities and differences. We identify and describe the main conceptual approaches, giving insight on their advantages and limitations, and we provide pointers to existing implementations. We initially focus on exact sequential algorithms, but we also do a thorough survey on approximate methodologies (with a trade-off between accuracy and execution time) and parallel strategies (that need to deal with an unbalanced search space).

Supervised
thesis

2021

Smart Transformer: Innovative Control Strategies to Improve Electric Power Systems Stability

Author
Justino Miguel Ferreira Rodrigues

Institution
UP-FEUP

2021

Data driven process improvement

Author
Maria Alexandra Ramalho de Oliveira

Institution
UP-FEUP

2021

Mobility Patterns from Data

Author
Thiago de Andrade Silva

Institution
UP-FEUP

2021

Incremental Temporal Interval Mining Methodologies

Author
Ana Micaela Gomes Batista

Institution
UP-FCUP

2020

Subgraph Counting in Graph Streams

Author
Henrique Jorge Santos Branquinho

Institution
UP-FCUP