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Details

  • Name

    Pedro Manuel Ribeiro
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    03rd May 2010
Publications

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
CoRR

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

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.

2020

PseudoChecker: an integrated online platform for gene inactivation inference

Authors
Alves, LQ; Ruivo, R; Fonseca, MM; Lopes Marques, M; Ribeiro, P; Castro, LFC;

Publication
Nucleic Acids Research

Abstract
Abstract The rapid expansion of high-quality genome assemblies, exemplified by ongoing initiatives such as the Genome-10K and i5k, demands novel automated methods to approach comparative genomics. Of these, the study of inactivating mutations in the coding region of genes, or pseudogenization, as a source of evolutionary novelty is mostly overlooked. Thus, to address such evolutionary/genomic events, a systematic, accurate and computationally automated approach is required. Here, we present PseudoChecker, the first integrated online platform for gene inactivation inference. Unlike the few existing methods, our comparative genomics-based approach displays full automation, a built-in graphical user interface and a novel index, PseudoIndex, for an empirical evaluation of the gene coding status. As a multi-platform online service, PseudoChecker simplifies access and usability, allowing a fast identification of disruptive mutations. An analysis of 30 genes previously reported to be eroded in mammals, and 30 viable genes from the same lineages, demonstrated that PseudoChecker was able to correctly infer 97% of loss events and 95% of functional genes, confirming its reliability. PseudoChecker is freely available, without login required, at http://pseudochecker.ciimar.up.pt.

2020

Condensed Graphs: A Generic Framework for Accelerating Subgraph Census Computation

Authors
Martins, M; Ribeiro, P;

Publication
Springer Proceedings in Complexity

Abstract
Determining subgraph frequencies is at the core of several graph mining methodologies such as discovering network motifs or computing graphlet degree distributions. Current state-of-the-art algorithms for this task either take advantage of common patterns emerging on the networks or target a set of specific subgraphs for which analytical calculations are feasible. Here, we propose a novel network generic framework revolving around a new data-structure, a Condensed Graph, that combines both the aforementioned approaches, but generalized to support any subgraph topology and size. Furthermore, our methodology can use as a baseline any enumeration based census algorithm, speeding up its computation. We target simple topologies that allow us to skip several redundant and heavy computational steps using combinatorics. We were are able to achieve substantial improvements, with evidence of exponential speedup for our best cases, where these patterns represent up to 97% of the network, from a broad set of real and synthetic networks. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Supervised
thesis

2020

Characterizing the Passing Networks of Football Teams

Author
Francisco Maria Castro Antunes Granja Bento

Institution
UP-FCUP

2020

Lightweight Real-time Feature Monitoring

Author
João Dias Conde Azevedo

Institution
UP-FEUP

2020

Towards Improving the Search for Multi-Relational Concepts in ILP

Author
Alberto José Rajão Barbosa

Institution
UP-FCUP

2020

Efficient General Subgraph Counting

Author
Luciano Polónia Gonçalves Grácio

Institution
UP-FCUP

2020

Network Analysis for Research Interests Discovery

Author
Jorge Miguel Barros da Silva

Institution
UP-FCUP