A Fusion Model of Network Representation Learning and Topic Model for Author Cooperation Prediction
张鑫,文奕,许海云
Zhang Xin,Wen Yi,Xu Haiyun
(Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu, 610041, China)
(Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China)
[Objective]This paper proposed a method of scientific collaboration prediction combing the fusion network representation learning and author topic model.
[Methods]Based on classical network representation learning method, the embedding vector representation of authors was calculated, and the structural similarity of authors was calculated by cosine similarity. Based on the author-topic model, the topic representation of authors was obtained, and the topic similarity of authors was calculated by Hellinger distance. Then the two similarity measures were fused linearly, and the Bayesian optimization method was used to fuse the hyperparameter selection.
[Results] Empirical research based on the NIPS datasets shows that after Bayesian parameter selection, the node2vec+ATM model achieves the AUC value of 0.9271, which improved 0.1856 than the benchmark model.
[Limitations] This article only considers the content of the author’s publications, but does not incorporate more attribute such as the author’s institution and geographic location into the model.
[Conclusions] The proposed fusion model take structure and content features into consideration and can improve the prediction effect of network representation learning.
张鑫, 文奕, 许海云.
一种融合表示学习与主题表征的作者合作预测模型
[J]. 数据分析与知识发现, 10.11925/infotech.2096-3467.2020.0515.
Zhang Xin, Wen Yi, Xu Haiyun.
A Fusion Model of Network Representation Learning and Topic Model for Author Cooperation Prediction
张鑫,文奕,许海云
. Data Analysis and Knowledge Discovery, 0, (): 1-.