一种融合表示学习与主题表征的作者合作预测模型  

A Prediction Model with Network Representation Learning and Topic Model for Author Collaboration

作　　者：张鑫 文奕[1,2] 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)

机构地区：[1]中国科学院成都文献情报中心,成都610041 [2]中国科学院大学经济与管理学院图书情报与档案管理系,北京100190

出　　处：《数据分析与知识发现》2021年第3期88-100,共13页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目(项目编号:71704170);中国科学院信息化专项(项目编号:XXH13506-203);中国科学院青年创新促进会项目(项目编号:2016159)的研究成果之一。

摘　　要：【目的】提出融合网络表示学习和作者主题模型的科研合作预测方法。【方法】基于经典网络表示学习方法计算得到作者节点的嵌入式向量表示,采用余弦相似度计算作者的结构相似性;基于作者主题模型计算得到作者的主题向量表征,采用Hellinger距离计算作者主题相似性。再将两种相似性方法进行线性特征融合,采用贝叶斯优化方法进行融合超参数选择。【结果】用NIPS论文数据进行实证研究,经过贝叶斯参数选择后效果最好的node2vec+ATM模型,预测的AUC值达到0.9271,比基准模型提高0.1856,也优于现有的一些融合外部信息的表示学习模型。【局限】仅考虑作者文章内容信息,没有将作者单位、地理位置等更多属性信息融入模型。【结论】本文提出的融合模型考虑了结构与内容特征,能够得到比简单网络表示学习更好的合作预测效果。[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.

关 键 词：网络表示学习 作者主题模型 模型融合 合作预测 

分 类 号：G350[文化科学—情报学]