IJERT-EMS
IJERT-EMS

A Novel Co-authorship Prediction Model using Semantic Clustering and Supervised Prediction


A Novel Co-authorship Prediction Model using Semantic Clustering and Supervised Prediction
Authors : Sivakumar P, Vipin Kumar K. S
Publication Date: 11-07-2017

Authors

Author(s):  Sivakumar P, Vipin Kumar K. S

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Volume. 6 - Issue. 07 , July - 2017

e-ISSN:   2278-0181

 DOI:  http://dx.doi.org/10.17577/IJERTV6IS070085

Abstract

Co-authorship prediction has been studied in researches as a part of social network analysis. Co-author prediction is the problem of predicting missing or future links (collaboratons) between authors. Previous studies have dealt with this problem and have proposed various approaches. Out of these, there are mainly two approaches: similarity based and learning-based. The former approach uses similarity metrics between authors such as common neighbor, random walks, etc and rank them while the latter treats co-author prediction as binary classification and uses learning models with similarity metrics as features. In this work, we propose a novel co-authorship prediction model based on semantic clustering and supervised learning. We test our proposed model with some other keyword-based predictors and the results show that our predictor performs averagely better than the comparison predictors.

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