OTARIOS: OpTimizing Author Ranking with Insiders/Outsiders Subnetworks

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Silva, JMB (Autor) Outra; Aparício, DO (Autor) Outra; Silva, F (Autor) FCUP

Ata de Conferência Internacional

Título: Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2018, Cambridge, UK, December 11-13, 2018.

Páginas: 143-154

Indexação

Scopus - 4 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-00P-YT4

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

Abstract (EN): Evaluating scientists based on their scientific production is often 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 focus on individual measures, disregarding the whole data (i.e., the whole network). Here we put forward OTARIOS, a graph-ranking method which combines multiple publication/citation criteria to rank authors. OTARIOS divides the original network in 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. When matching a metric¿s produced ranking with best papers awards received, we observe that OTARIOS is >20 more accurate than traditional bibliometrics. We obtain the best results when OTARIOS considers (i) the author¿s publication volume and publication recency, (ii) how recently his work is being cited by outsiders, and (iii) how recently his work is being cited by insiders and how individual he his. © 2019, Springer Nature Switzerland AG.

Idioma: Inglês

Tipo (Avaliação Docente): Científica