Research collaboration model in academic social networks

ABSTRACT

This paper proposes a hybrid collaboration recommendation method that accounts for research similarities and the previous research cooperation network. Research cooperation is measured by combining the collaboration time and the number of co-authors who already collaborated with at least one scientist. Research similarity is based on authors’ previous publications and academic events they attended. A weighted directed graph is built to discover new collaborators by using direct and indirect connections between scientists. Moreover, a consensus-based system is built to integrate bibliography data from different sources. The experimental results show that our method improves the recommendation performances over other methods.

KEYWORDS:

• Collaboration model
• research collaboration
• academic social networks
• academic data integration

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. http://dblp.uni-trier.de/.

2. http://citeseerx.ist.psu.edu/.

3. https://www.researchgate.net/.

4. https://pypi.python.org/pypi/bibtexparser.

5. http://dblp.uni-trier.de/xml/.

6. https://dev.elsevier.com/user/login.

7. http://wikicfp.com/examples/wikicfp.v1.2008.xml.gz.

8. http://wikicfp.com/examples/wikicfp.v1.2009.xml.gz.

9. http://wikicfp.com/examples/wikicfp.v1.2010.xml.gz.

Additional information

Funding

This work was supported by the 2016 Yeungnam University Research Grant.