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Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features

Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features

Manju G., Kavitha V., Geetha T.V.
Copyright: © 2017 |Volume: 13 |Issue: 1 |Pages: 25
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781522511458|DOI: 10.4018/IJIIT.2017010101
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MLA

G., Manju, et al. "Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features." IJIIT vol.13, no.1 2017: pp.1-25. http://doi.org/10.4018/IJIIT.2017010101

APA

G., M., V., K., & T.V., G. (2017). Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features. International Journal of Intelligent Information Technologies (IJIIT), 13(1), 1-25. http://doi.org/10.4018/IJIIT.2017010101

Chicago

G., Manju, Kavitha V., and Geetha T.V. "Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features," International Journal of Intelligent Information Technologies (IJIIT) 13, no.1: 1-25. http://doi.org/10.4018/IJIIT.2017010101

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Abstract

Researchers entering into a new research area are interested in knowing the current research trends, popular publications and influential (popular) researchers in that area in order to initiate their research. In this work, we attempt to determine the influential researcher for a specific topic. The active participation of the researchers in both the academic and social network activities signifies the researchers' influence level across time. The content and frequency of social interaction to a researcher reflects his or her influence. In our system, appropriate time-based social and academic features are selected using entropy based feature selection approach of rough set theory. A three layer model comprising semantically related concepts, researcher and social relations is developed based on the appropriate (influential) features. The researchers' topic trajectories are identified and recommended using Spreading activation algorithm. To cope up with the scalable academic network, map reduce paradigm has been employed in the spreading activation algorithm.

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