Predicting Collaborations among Research Scientists: A Datathon Experience
Year: 2025
Type: article
Abstract: This paper presents the challenge of predicting collaborations between research scientists as a datathon experience. The focus of the challenge task is determining whether or not the author of a research paper is keen to collaborate with another author in the future. The main aims of the datathon challenge are: (i) to show the feasibility of automatically identifying potential collaboration in a research network as a link prediction task; (ii) to propose a methodology for the environment configuration that covers data collection, selection and preparation stages required for link prediction in a massive event. (iii) to join the efforts of students from different fields of study in solving the task from a multi-disciplinary perspective. For this purpose, we created a corpus with DBLP data covering publications from 1990 to 2004. The created dataset has been made available for further research. Altogether, the datathon attracted 78 registered students, yielding 13 submissions of teams composed of 6 students each. In this paper, we compare their approaches and analyze their performance.
Authors: Maria Del Pilar Angeles, Helena Gomez Adorno, Sinuhe David Hernandez Guevara, Victor Manuel Corza Vargas
Institution: Universidad Nacional Autónoma de México
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4408948350