%A Zhang Xin,Wen Yi,Xu Haiyun %T A Prediction Model with Network Representation Learning and Topic Model for Author Collaboration %0 Journal Article %D 2021 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2020.0515 %P 88-100 %V 5 %N 3 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4948.shtml} %8 2021-03-25 %X

[Objective] This paper proposes a method to predict scientific collaboration based on the network representation learning and author topic model. [Methods] First, we established the embedding vector representation of authors with the help of network representation learning method. Then, we calculated the structural similarity of authors with cosine similarity. Third, we obtained the topic representation of authors with the author-topic model, and computed the authors’ topic similarity with Hellinger distance. Finally, we linearly merged the two similarity measures, and used the Bayesian optimization method for the hyperparameter selection. [Results] We examined the proposed method with the NIPS datasets and found the best node2vec+ATM model after Bayesian parameter selection. It had an AUC value of 0.9271, which was 0.1856 higher than that of the benchmark model. [Limitations] We did not include the author’s institution and geographic location to the model. [Conclusions] The proposed model utilizes structure and content features to improve the prediction results of network representation learning.