Link prediction in co-authorship networks using scopus data

Erik Medina-Acuña, Pedro Shiguihara-Juárez, Nils Murrugarra-Llerena

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Link Prediction is a common task for social networks and recommendation systems. In this paper, we study the problem of link prediction on Scopus co-authorship networks. We used many well-known relational features, and evaluate them with five different classifiers. Finally, we perform a feature analysis to determine the most crucial features in this setup.

Original languageEnglish
Title of host publicationInformation Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings
EditorsDenisse Muñante, Hugo Alatrista-Salas, Juan Antonio Lossio-Ventura
PublisherSpringer Verlag
Pages91-97
Number of pages7
ISBN (Print)9783030116798
DOIs
StatePublished - 2019
Externally publishedYes
Event5th International Conference on Information Management and Big Data, SIMBig 2018 - Lima, Peru
Duration: 3 Sep 20185 Sep 2018

Publication series

NameCommunications in Computer and Information Science
Volume898
ISSN (Print)1865-0929

Conference

Conference5th International Conference on Information Management and Big Data, SIMBig 2018
Country/TerritoryPeru
CityLima
Period3/09/185/09/18

Keywords

  • Co-authorship network
  • Data mining
  • Decision trees
  • Link prediction
  • Machine learning
  • Supervised learning

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