OTARIOS: OpTimizing Author Ranking with Insiders/Outsiders Subnetworks

Silva, Jorge M. B.; Aparício, David; Silva, Fernando M. A.

doi:10.1007/978-3-030-05411-3_12

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Although there is no ideal solution for this problem, one can be more careful in estimating the rank of nodes in the periphery. Table 2 summarises current STOA methods and contrasts them with OTARIOS, our proposed method (Silva et al 2018). OTARIOS is the only method that efficiently combines multiple features (of the publications and citations) and deals with incomplete networks.…”

Section: State-of-the-art Author Ranking Methodsmentioning

confidence: 99%

“…We recently proposed a new feature enriched author ranking algorithm for incomplete networks named OTARIOS (OpTimizing Author Ranking with Insiders/Outsiders Subnetworks) (Silva et al 2018), and showed that OTARIOS outperformed traditional PageRank (a topology-based method) and simple bibliometrics. Here we compare OTARIOS against other feature-enriched author ranking methods.…”

Section: Introductionmentioning

confidence: 99%

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Feature-enriched author ranking in incomplete networks

2019

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Evaluating scientists based on their scientific production is a controversial topic. Nevertheless, bibliometrics and algorithmic approaches can assist traditional peer review in numerous tasks, such as attributing research grants, deciding scientific committees, or choosing faculty promotions. Traditional bibliometrics rank individual entities (e.g., researchers, journals, faculties) without looking at the whole data (i.e., the whole network). Network algorithms, such as PageRank, have been used to measure node importance in a network, and have been applied to author ranking. However, traditional PageRank only uses network topology and ignores relevant features of scientific collaborations. Multiple extensions of PageRank have been proposed, more suited for author ranking. These methods enrich the network with information about the author's productivity or the venue and year of the publication/citation. Most state-of-the-art (STOA) feature-enriched methods either ignore or do not combine effectively this information. Furthermore, STOA algorithms typically disregard that the full network is not known for most real-world cases. Here we describe OTARIOS, an author ranking method recently developed by us, which combines multiple publication/citation criteria (i.e., features) to evaluate authors. OTARIOS divides the original network into two subnetworks, insiders and outsiders, which is an adequate representation of citation networks with missing information. We evaluate OTARIOS on a set of five real networks, each with publications in distinct areas of Computer Science, and compare it against STOA methods. When matching OTARIOS' produced ranking with a ground-truth ranking (comprised of best paper award nominations), we observe that OTARIOS is > 30% more accurate than traditional PageRank (i.e., topology based method) and > 20% more accurate than STOA (i.e., competing feature enriched methods). We obtain the best results when OTARIOS considers (i) the author's publication volume and publication recency, (ii) how recently the author's work is being cited by outsiders, and (iii) how recently the author's work is being cited by insiders and how individual he is. Our results showcase (a) the importance of efficiently combining relevant features and (b) how to adequately perform author ranking in incomplete networks.

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Section: State-of-the-art Author Ranking Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature-enriched author ranking in incomplete networks

2019

Self Cite

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Finding Dominant Nodes Using Graphlets

Aparício

Ribeiro

Silva

et al. 2019

Complex Networks and Their Applications VIII

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Citation Oriented AuthorRank for Scientific Publication Ranking

Zhang

Liu

2022

Applied Sciences

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It is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. Actually, in scientific publications, the author is an important metadata item, which has been widely used in previous studies. In this paper, we bring an optimized AuthorRank, which is a topic-sensitive algorithm calculated by citation context, into citation analysis for testing whether and how topical AuthorRank can replace or enhance classical PageRank for publication ranking. For this purpose, we first propose a PageRank with Priors (PRP) algorithm to rank publications and authors. PRP is an optimized PageRank algorithm supervised by the Labeled Latent Dirichlet Allocation (Labeled-LDA) topic model with full-text information extraction. We then compared four methods of generating an AuthorRank score, looking, respectively, at the first author, the last author, the most famous author, and the “average” author (of a publication). Additionally, two combination methods (Linear and Cobb–Douglas) of AuthorRank and PRP were compared with several baselines. Finally, as shown in our evaluation results, the performance of AuthorRank combined with PRP is better (p < 0.001) than other baselines for publication ranking.

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OTARIOS: OpTimizing Author Ranking with Insiders/Outsiders Subnetworks

Cited by 3 publications

References 10 publications

Feature-enriched author ranking in incomplete networks

Feature-enriched author ranking in incomplete networks

Finding Dominant Nodes Using Graphlets

Citation Oriented AuthorRank for Scientific Publication Ranking

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