Co-author Relationship Prediction in Bibliographic Network

Abstract

In this research, we propose a novel approach for co-author relationship prediction in a bibliographic network utilizing geographic factor and latent topic information. We utilize a supervised method to predict the co-author relationship formation where combining dissimilar features with the dissimilar measuring coefficient. Firstly, besides existing relations have been studied in previous researches, we exploit new relation related to the geographic factor which contributes as a topological feature. Moreover, we discover content feature based on textual information from author's papers using topic modeling. Finally, we amalgamate topological features and content feature in co-author relationship prediction. We conducted experiments on dissimilar datasets of the bibliographic network and have attained satisfactory results.