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Big Bibliographic Data Analytics by Random Walk Model

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Abstract

In this paper we use the DBLP data to investigate the co-author relationship in a real bibliographic network and predict the interactions between co-authors. We analysis the research trend of authors and conferences based on keywords extracted from paper titles. We can understand research fields and change of research trend to find appropriate co-authors and conferences to submit our work. We also find potential co-authors for an existing author in DBLP data by using a variety of similarity measures and a random walk model. It can be useful for building a recommendation system.

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2014R1A2A2A05007154). Also, this research was supported by the MSIP (Ministry of Science, ICT&Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2014-H0301-14-1044) supervised by the NIPA (National ICT Industry Promotion Agency).

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  1. School of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea

    Jason J. Jung

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  1. Jason J. Jung
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Correspondence to Jason J. Jung.

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Jung, J.J. Big Bibliographic Data Analytics by Random Walk Model. Mobile Netw Appl 20, 533–537 (2015). https://doi.org/10.1007/s11036-014-0555-2

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