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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

2021

Time series analysis via network science: Concepts and algorithms

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

Publication
WIREs 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).

2020

FOCAS: Penalising friendly citations to improve author ranking

Authors
Silva, J; Aparicio, D; Ribeiro, P; Silva, F;

Publication
Proceedings of the ACM Symposium on Applied Computing

Abstract
Scientific impact is commonly associated with the number of citations received. However, an author can easily boost his own citation count by (i) publishing articles that cite his own previous work (self-citations), (ii) having co-authors citing his work (co-author citations), or (iii) exchanging citations with authors from other research groups (reciprocated citations). Even though these friendly citations inflate an author's perceived scientific impact, author ranking algorithms do not normally address them. They, at most, remove self-citations. Here we present Friends-Only Citations AnalySer (FOCAS), a method that identifies friendly citations and reduces their negative effect in author ranking algorithms. FOCAS combines the author citation network with the co-authorship network in order to measure author proximity and penalises citations between friendly authors. FOCAS is general and can be regarded as an independent module applied while running (any) PageRank-like author ranking algorithm. FOCAS can be tuned to use three different criteria, namely authors' distance, citation frequency, and citation recency, or combinations of these. We evaluate and compare FOCAS against eight state-of-the-art author ranking algorithms. We compare their rankings with a ground-truth of best paper awards. We test our hypothesis on a citation and co-authorship network comprised of seven Information Retrieval top-conferences. We observed that FOCAS improved author rankings by 25% on average and, in one case, leads to a gain of 46%. © 2020 ACM.

Supervised
thesis

2021

Multidimensional Time Series Analysis: A Complex Networks Approach

Author
Vanessa Alexandra Freitas da Silva

Institution
UP-FCUP

2021

Towards Improving the Search for Multi-Relational Concepts in ILP

Author
Alberto José Rajão Barbosa

Institution
UP-FCUP

2021

Fraud Detection and Anti-money Laundering using Graph Techniques

Author
Ahmad Naser Eddin

Institution
UP-FCUP

2021

Building Blocks of Networks

Author
Luciano Polónia Gonçalves Grácio

Institution
UP-FCUP

2021

Subgraph Patterns in Colored Networks

Author
Beatriz Maria Franco Pinto

Institution
UP-FCUP