TY - GEN
T1 - Link prediction in co-authorship networks using scopus data
AU - Medina-Acuña, Erik
AU - Shiguihara-Juárez, Pedro
AU - Murrugarra-Llerena, Nils
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Co-authorship network
KW - Data mining
KW - Decision trees
KW - Link prediction
KW - Machine learning
KW - Supervised learning
UR - https://www.scopus.com/pages/publications/85063539747
U2 - 10.1007/978-3-030-11680-4_10
DO - 10.1007/978-3-030-11680-4_10
M3 - Contribución a la conferencia
AN - SCOPUS:85063539747
SN - 9783030116798
T3 - Communications in Computer and Information Science
SP - 91
EP - 97
BT - Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings
A2 - Muñante, Denisse
A2 - Alatrista-Salas, Hugo
A2 - Lossio-Ventura, Juan Antonio
PB - Springer Verlag
T2 - 5th International Conference on Information Management and Big Data, SIMBig 2018
Y2 - 3 September 2018 through 5 September 2018
ER -