[1]朱乔亚,陈可佳,方彪. 采用位置信息的半监督链接预测方法[J].计算机技术与发展,2015,25(07):63-66.
　ZHU Qiao-ya,CHEN Ke-jia,FANG Biao. A Semi-supervised Link Prediction Method Using Place Features[J].,2015,25(07):63-66.
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 采用位置信息的半监督链接预测方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年07期
页码:
63-66
栏目:
智能、算法、系统工程
出版日期:
2015-07-10

文章信息/Info

Title:
 A Semi-supervised Link Prediction Method Using Place Features
文章编号:
1673-629X（2015)07-0063-04
作者:
 朱乔亚; 陈可佳; 方彪
 南京邮电大学 计算机学院
Author(s):
 ZHU Qiao-ya; CHEN Ke-jia; FANG Biao
关键词:
 ; 基于位置的网络; 链接预测; 半监督学习; 社会网络分析
Keywords:
 ; location-based network; link prediction; semi-supervised learning; social network analysis
分类号:
TP39
文献标志码:
A
摘要:
 链接预测是社会网络分析领域的一个关键问题，如何从网络的已知信息有效预测网络的未知信息面临巨大的挑战。为了有效利用网络中大量未连接的节点及节点对信息，文中将节点的位置信息(签到信息)加入到线社交网络中，并将节点的位置信息引入基于半监督的链接预测方法( LB-SSLP方法)，根据用户之间的关系以及位置签到信息预测用户未来可能的签到位置，同时与传统的SSLP方法和SLP方法进行对比。在现实数据集Gowalla中的实验结果表明，位置信息的引入以及半监督学习的使用均能有效提高链接预测方法的准确率。
Abstract:
 Link prediction is a key issue in the research area of social network analysis,aiming to predict unknown links from the known network. In order to make full use of the information of nodes and node pairs with no connection,add the place features to the online so-cial networks ( check-ins) and join it to a semi-supervised link prediction method ( LB-SSLP) so that the future possible location of a user can be predicted according to the relationship with other users and his check-ins,at the same time,compare with the traditional SSLP method and SLP method. The experimental results in a real dataset Gowalla show that both the use of place features and semi-supervised learning make the proposed method perform a higher prediction accuracy.

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更新日期/Last Update: 2015-09-06