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Details

  • Name

    Fernando Silva
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st January 2009
009
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

Jay: Adaptive Computation Offloading for Hybrid Cloud Environments

Authors
Silva, J; Marques, ERB; Lopes, LMB; Silva, F;

Publication
2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)

Abstract

2019

Temporal network alignment via GoT-WAVE

Authors
Aparicio, D; Ribeiro, P; Milenkovic, T; Silva, F;

Publication
Bioinformatics

Abstract

Supervised
thesis

2020

Scheduling Computations Over High Churn Networks of Mobile Devices

Author
Joaquim Magalhães Esteves da Silva

Institution
UP-FCUP

2020

Network Analysis for Research Interests Discovery

Author
Jorge Miguel Barros da Silva

Institution
UP-FCUP

2019

Network Comparison and Node Ranking in Complex Networks

Author
David Oliveira Aparício

Institution
UP-FCUP

2019

Network Analysis for Research Interests Discovery

Author
Jorge Miguel Barros da Silva

Institution
UP-FCUP

2019

Scheduling Computations Over High Churn Networks of Mobile Devices

Author
Joaquim Magalhães Esteves da Silva

Institution
UP-FCUP