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Details

  • Name

    Fernando Silva
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st January 2009
009
Publications

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).

2021

Energy-aware adaptive offloading of soft real-time jobs in mobile edge clouds

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

Publication
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS

Abstract
We present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices, in cloudlets or in infrastructure cloud servers. Within this specification, we put forward several such offloading strategies characterised by their differential use of the cloud tiers with the goal of optimizing execution time and/or energy consumption. We implement an instance of the model using Jay, a software framework for adaptive computation offloading in hybrid edge clouds. The framework is modular and allows the model and the offloading strategies to be seamlessly implemented while providing the tools to make informed runtime offloading decisions based on system feedback, namely through a built-in system profiler that gathers runtime information such as workload, energy consumption and available bandwidth for every participating device or server. The results show that offloading strategies sensitive to runtime conditions can effectively and dynamically adjust their offloading decisions to produce significant gains in terms of their target optimization functions, namely, execution time, energy consumption and fulfilment of job deadlines.

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

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