Details
Name
Pedro Manuel RibeiroCluster
Computer ScienceRole
Senior ResearcherSince
03rd May 2010
Nationality
PortugalCentre
Advanced Computing SystemsContacts
+351220402963
pedro.p.ribeiro@inesctec.pt
2020
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
Authors
Alves, LQ; Ruivo, R; Fonseca, MM; Lopes Marques, M; Ribeiro, P; Castro, LFC;
Publication
Nucleic Acids Research
Abstract
2020
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.
2019
Authors
Aparicio, D; Ribeiro, P; Milenkovic, T; Silva, F;
Publication
Bioinformatics
Abstract
2019
Authors
Araujo, M; Ribeiro, P; Song, HA; Faloutsos, C;
Publication
KNOWLEDGE AND INFORMATION SYSTEMS
Abstract
Given an heterogeneous social network, can we forecast its future? Can we predict who will start using a given hashtag on twitter? Can we leverage side information, such as who retweets or follows whom, to improve our membership forecasts? We present TENSORCAST, a novel method that forecasts time-evolving networks more accurately than current state-of-the-art methods by incorporating multiple data sources in coupled tensors. TENSORCAST is (a) scalable, being linearithmic on the number of connections; (b) effective, achieving over 20% improved precision on top-1000 forecasts of community members; (c) general, being applicable to data sources with different structure. We run our method on multiple real-world networks, including DBLP, epidemiology data, power grid data, and a Twitter temporal network with over 310 million nonzeros, where we predict the evolution of the activity of the use of political hashtags.
Supervised Thesis
2019
Author
Alberto José Rajão Barbosa
Institution
UP-FCUP
2019
Author
Luís Pedro Palmares Quádrio Alves
Institution
UP-FCUP
2019
Author
André Couto Meira
Institution
UP-FCUP
2019
Author
Vanessa Alexandra Freitas da Silva
Institution
UP-FCUP
2019
Author
David Oliveira Aparício
Institution
UP-FCUP
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