How we collaborate: characterizing, modeling and predicting scientific collaborations
Authored by Xiaoling Sun, Hongfei Lin, Kan Xu, Kun Ding
Date Published: 2015
DOI: 10.1007/s11192-015-1597-3
Sponsors:
Chinese National Natural Science Foundation
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
The large amounts of publicly available bibliographic repositories on
the web provide us great opportunities to study the scientific behaviors
of scholars. This paper aims to study the way we collaborate, model the
dynamics of collaborations and predict future collaborations among
authors. We investigate the collaborations in three disciplines
including physics, computer science and information science,and
different kinds of features which may influence the creation of
collaborations. Path-based features are found to be particularly useful
in predicting collaborations. Besides, the combination of path-based and
attribute-based features achieves almost the same performance as the
combination of all features considered. Inspired by the findings, we
propose an agent-based model to simulate the dynamics of collaborations.
The model merges the ideas of network structure and node attributes by
leveraging random walk mechanism and interests similarity. Empirical
results show that the model could reproduce a number of realistic and
critical network statistics and patterns. We further apply the model to
predict collaborations in an unsupervised manner and compare it with
several state-of-the-art approaches. The proposed model achieves the
best predictive performance compared with the random baseline and other
approaches. The results suggest that both network structure and node
attributes may play an important role in shaping the evolution of
collaboration networks.
Tags
Social networks
Evolution
Small-world networks