摘要
移动社交网络中的链路预测是指通过已知的网络节点以及移动社交网络结构等信息预测网络中尚未产生连边的两个节点之间产生链接的可能性。基于网络中心度的思想,提出一种适用于移动社交网络的链路预测算法。该算法利用节点网络中心度和共同邻居数来计算两个节点的相似性指标,两个节点的共同邻居数越多、共同邻居的网络中心度越高,则两个节点的相似度越高。另外,由于移动社交网络的动态性特征,还将考虑时间因素对预测结果的影响。将该方法与其他四种常用的链路预测方法进行比较,实验结果显示所提方法要优于其他方法。
Link prediction aims at estimating the likelihood of the existence of links between nodes in mobile social networks.This paper presented an improved algorithm for mobile social networks based on centrality of common neighbors. In this method,it used the network centrality and the number of common neighbors to calculate the node similarity. If the two nodes had more common neighbors and the node centrality of the common neighbors was higher,the similarity of two nodes were higher. In addition,this paper also considered the temporal information due to the fact that the behavior of links as time goes by. The experimental results show that the performance of the improved algorithm is superior to the four traditional link prediction algorithms.
作者
郑巍
潘倩
邓宇凡
Zheng Wei;Pan Qian;Deng Yufan(College of Software, Nanchang Hangkong University, Nanchang 330063 , China)
出处
《计算机应用研究》
CSCD
北大核心
2016年第9期2743-2746,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(61363015
61262020)
国家自然科学青年基金资助项目(61501217)
江西省自然科学基金资助项目(20142BAB206026)
江西省教育厅青年科学基金项目(GJJ12457
GJJ13482)
关键词
网络中心度
共同邻居
链路预测
移动社交网络
network centrality
common neighbors
link prediction
mobile social networks(MSNs)
