Similarity-based Complex Publication Network Analytics for Recommending Potential Collaborations

Ngoc Tu Luong (Yeungnam University, Korea)

Tuong Tri Nguyen (Yeungnam University, Korea)

Dosam Hwang (Yeungnam University, Korea)

Chang Ha Lee (Chung-Ang University, Korea)

Jason J. Jung (Chung-Ang University, Korea)

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, HITS algorithm, Random Walk Model, scientists searching

Categories: H.1.1, H.3.5, I.2.11