Predicting Scientist Collaboration by Multiple Motif Features

Datos Bibliográficos

ID22107751
AutoresShuang Xu (0000-0002-3470-0609, Dalian Minzu University), Yijun Ran (0000-0002-7047-3343, Southwest University), Xiao-Ke Xu (0000-0002-9148-3145, Dalian Minzu University)
Año2023
Volumen10
Número4
Páginas1826-1834
Fecha de publicación2023-08-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaIEEE Transactions on Computational Social Systems (JOURNAL)
Identificadores de la revistaISSN: 2329-924X • E-ISSN: 2373-7476
EditorialInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2022.3144247
OpenAlexW4210815143
IdiomaEN
Citas recibidas2
Referencias citadas34

Scientist collaboration is of great significance for knowledge production and scientific development, and the prediction of connection and the intensity in collaboration networks are essential to understand collaboration relationships between scientists. In previous studies, most scholars only use local structure similarity to infer scientist collaboration modes, which leads to failure to accurately predict collaboration relationships between scientists. In this study, we propose a prediction method to identify missing links and link weight by using multiple motif features. The experimental results show that the highest improvement of performance in link prediction is 13.5%, and 86.8% in weight prediction. In addition, the correlation analysis on multiple motif features reveals topology correlation between different scientist collaboration modes. Our finding is helpful to predict link possibility and tie strength in scientist collaboration networks more accurately and understand deeply the evolution pattern of collaboration networks among scientists

Complex network · Correlation · Data mining · Data science · Physics · World Wide Web · Bioinformatics and Genomic Networks · Complex Network Analysis Techniques · Computer Science · Mathematics · scientometrics and bibliometrics research · Artificial Intelligence