Abstract
Korean National Science & Technology Information Service (NTIS) provides a service of evaluating national R&D projects and providing such evaluated national R&D projects along with their participating researcher information. It also provides a service of recommending and selecting evaluation committees for the R&D projects. Transparency is an important aspect that should be ensured on the evaluation process of the national R&D projects. Thus, the recommending unfamiliar evaluation committees with the participants of the R&D projects are one of the important aspects that can ensure the transparency for the evaluation process. In this paper, we present an evaluation-committee recommendation system using an online detection method of researcher connections by a partitioning-based clustering algorithm and random walks. The clustering algorithm enables us to partition the network to number of small graphs that can be processed via random walks. Then, we can rank the connection weight of each suspicious researcher according to a researcher in charge of a R&D project and we can exclude the researchers having higher connection weight from the evaluation committee of the R&D project. In addition, we also present a text-data refinement and entity identification method using Jaro–Winkler distance algorithm to construct more precise researcher network.
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This research was supported by Maximize the Value of National Science and Technology by Strengthen Sharing/Collaboration of National R&D Information funded by the Korea Institute of Science and Technology Information (KISTI).
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Jeong, H., Kim, YK. & Kim, J. An evaluation-committee recommendation system for national R&D projects using social network analysis. Cluster Comput 19, 921–930 (2016). https://doi.org/10.1007/s10586-016-0545-1
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DOI: https://doi.org/10.1007/s10586-016-0545-1