JUCS - Journal of Universal Computer Science 21(6): 871-889, doi: 10.3217/jucs-021-06-0871
Similarity-based Complex Publication Network Analytics for Recommending Potential Collaborations
Ngoc Tu Luong‡, Tuong Tri Nguyen‡, Dosam Hwang‡, Chang Ha Lee§, Jai E. Jung§‡ Yeungnam University, Gyeongsan, Republic of Korea§ Chung-Ang University, Seoul, Republic of Korea
Corresponding author: Ngoc Luong ( tuln@gmail.com ) © Ngoc Luong, Tuong Nguyen, Dosam Hwang, Chang Lee, Jai Jung. This article is freely available under the J.UCS Open Content License. Citation:
Luong NT, Nguyen TT, Hwang D, Lee CH, Jung JE (2015) Similarity-based Complex Publication Network Analytics for Recommending Potential Collaborations. JUCS - Journal of Universal Computer Science 21(6): 871-889. https://doi.org/10.3217/jucs-021-06-0871 |  |
Abstract
As communities of researchers continue to become quite large and to grow incessantly, collaboration among researchers can be conducive to greater research productivity. Nevertheless, it is difficult for a researcher to find suitable collaborators from all researchers around the world. In this paper, we have used bibliographic DBLP data to extract information of a researcher and to discover the relationship between the co-authors and between authors and conferences. We evaluated some of the similarity measures and developed an innovative random walk model to find potential co-authors for a given researcher. These measures were then used to design a best model to recommend co-authors. We have also applied an HITS algorithm and proposed a ranking algorithm to rank researchers and conferences with the intent of recommending authors or conferences.
Keywords
DBLP database, scientists searching, HITS algorithm, Random Walk Model