Link prediction in author collaboration network based on BP neural network

Chaoqun Chen, Sanhong Deng, Jing Lu, Bing Xu, Yinong Chen
2017 MATEC Web of Conferences  
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
more » ... ata 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. the task of link prediction is divided into two categories: one is to predict the link that will appear in the future time, the second one is to predict the hidden unknown link in the space, this article discusses the former. Link prediction has two main approaches: a score-based approach and a machine learning approach. The score-based approach is to consider link predictions as a regression problem by calculating the similarity scores for each pair of nodes and then sorting them, the order determines the likelihood of forming future links. The score-based approach is a simple and effective method, but this approach is sensitive to different features' weights. The machine learning approach is effective to use a variety of attributes to predict the formation of links, and does not need to give the weight of each feature manually, and it is easy to expand. Therefore, the method of machine learning has been widely used in link prediction. The machine learning approach regards the link prediction problem as a
doi:10.1051/matecconf/201713900073 fatcat:u5vt4h2ukve35ivswlspy6if4m