计算机应用与软件2017，Vol.34Issue(10)：241-247,7.DOI:10.3969/j.issn.1000-386x.2017.10.043
基于SkipGram模型的链路预测方法
A LINK PREDICTION METHOD BASED ON SKIPGRAM MODEL
摘要
Abstract
The existing link prediction algorithm based on node similarity can hardly keep low complexity of the computation when aiming to promote prediction accuracy.Inspired by the application of probabilistic graphical model of natural language,this paper proposes a link prediction method based on SkipGram model.First,the random walk based on probability method was proposed,and the sampling sequence of the network nodes was obtained by this method.Then,the network nodes were mapped to a low dimensional vector space to reduce the complexity by combining the SkipGram model.In the end,the distance between vectors was used as the index to measure the similarity between the nodes of the network to accomplish link prediction.Through experiments and comparison in six representative real networks,the model proposed in this paper can improve the accuracy of prediction a lot.关键词
链路预测/向量表征/SkipGram模型/节点相似性Key words
Link prediction/Vector representation/SkipGram model/Node similarity分类
信息技术与安全科学引用本文复制引用
赵超,朱福喜,刘世超..基于SkipGram模型的链路预测方法[J].计算机应用与软件,2017,34(10):241-247,7.基金项目
国家自然科学基金项目(61272277). （61272277）
