摘要
针对文献网络节点间的关系预测问题,将节点相似度作为节点间关系概率,采用网络表示学习的方法将文献网络中的节点嵌入到低维空间后计算节点相似度,同时提出基于元结构的网络表示学习模型.根据节点间基于不同元结构的相关性,融合相应的特征表示,将网络映射到低维的特征空间.在低维特征空间内进行距离度量,实现文献网络中的关系预测.实验表明文中模型在文献网络中可得到良好的关系预测结果.
To solve the problem of relationship prediction among literature network nodes,the similarity of nodes is regarded as the probability of relationship among nodes,and a network representation learning method is utilized to embed nodes into a low-dimensional space to calculate the similarity.Therefore,a meta-structure-based network representation learning model is proposed.According to the correlation between nodes based on different meta-structures,the network is mapped to a low-dimensional feature space by fusing their corresponding feature representations.The relationship prediction of literature network is realized by the distance measure in the low-dimensional feature space.Experiments indicate that the proposed algorithm obtains good relationship prediction results in literature network.
作者
王秀
陈璐
余春艳
WANG Xiu;CHEN Lu;YU Chunyan(College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350108)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2020年第3期277-286,共10页
Pattern Recognition and Artificial Intelligence
基金
福建省自然科学基金项目(No.2015J01420)
福建省引导性基金项目(No.2016Y0060)
福建省卫生教育联合攻关计划项目(No.WKJ2016-2-26)资助。
关键词
文献网络
关系预测
元结构
网络表示学习
Literature Network
Relationship Prediction
Meta-Structure
Network Representation Learning
