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.
Volume/Issue: Volume. 6 - Issue. 07 , July - 2017
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.
Number of Citations for this article: Data not Available
7 Paper(s) Found related to your topic:
Publish your Ph.D/Master's Thesis Online