Title
Link prediction in co-authorship networks using scopus data
Date Issued
01 January 2019
Access level
metadata only access
Resource Type
conference paper
Author(s)
Publisher(s)
Springer Verlag
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.
Start page
91
End page
97
Volume
898
Language
English
OCDE Knowledge area
Ciencias de la computación
Ingeniería de sistemas y comunicaciones
Subjects
Scopus EID
2-s2.0-85063539747
Source
Communications in Computer and Information Science
Resource of which it is part
Communications in Computer and Information Science
ISSN of the container
18650929
ISBN of the container
9783030116798
Conference
5th International Conference on Information Management and Big Data, SIMBig 2018 Lima 3 September 2018 through 5 September 2018
Sources of information:
Directorio de Producción Científica
Scopus