Link prediction in author collaboration network based on BP neural network

Abstract

Recently, more and more authors have been encouraged for collaboration because it often produces good results. However, the author collaboration network contains experts in various research directions within various fields, and it is difficult for individual authors to decide which authors are best suited to their expertise. This paper uses the relationships among authors to predict new relationships that may arise, recommending each author with the collaborators they may be interested in. The data source comes from 4-year data in DBLP from 2001 to 2004. After data cleaning, the training set and test set are constructed and then used BP neural network to build model. At the same time, this article compares the performance with Logistic Regression, SVM and Random Forest. The experiment shows that the BP neural network can get better result, and it is feasible to predict links in the author collaboration network.