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Enhancement of co-authorship networks with content-similarity information

Published: 03 August 2012 Publication History

Abstract

Co-authorship networks contain a wealth of information from the collaboration of the researchers, the research areas of individual authors to the detection of research communities and much more. Its applications in the field of learning and research include expert detection in a research area to the identification of institutions that involve in research on any particular area. With the exponential rise in research in the recent past and the enormity of the Co-authorship networks, research on mining knowledge from them has been an active area of research. We propose an enhancement to the co-authorship network. New edges are introduced based on the similarity within abstracts. This model would help in identification of potential research synergies even when the authors don't share a common edge in the original co-authorship graph. The initial experiments show that the modifications in the graph improve the co-authorship network in a semantic perspective.

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Cited By

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  • (2018)Measuring Authorial Indices from the Eye of Co-author(s)Smart Innovations in Communication and Computational Sciences10.1007/978-981-10-8968-8_36(423-433)Online publication date: 19-Jun-2018
  • (2014)Context Based Retrieval of Scientific Publications via Reader LensComputational Intelligence in Data Mining - Volume 310.1007/978-81-322-2202-6_53(583-596)Online publication date: 12-Dec-2014

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cover image ACM Other conferences
ICACCI '12: Proceedings of the International Conference on Advances in Computing, Communications and Informatics
August 2012
1307 pages
ISBN:9781450311960
DOI:10.1145/2345396
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Published: 03 August 2012

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View all
  • (2018)Measuring Authorial Indices from the Eye of Co-author(s)Smart Innovations in Communication and Computational Sciences10.1007/978-981-10-8968-8_36(423-433)Online publication date: 19-Jun-2018
  • (2014)Context Based Retrieval of Scientific Publications via Reader LensComputational Intelligence in Data Mining - Volume 310.1007/978-81-322-2202-6_53(583-596)Online publication date: 12-Dec-2014

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