教授 博士生导师 硕士生导师
主要任职: 科学技术研究院国防重大项目办公室主任
性别: 男
毕业院校: 中国科技大学
学位: 博士
所在单位: 软件学院、国际信息与软件学院
学科: 计算机应用技术. 软件工程
电子邮箱: xczhang@dlut.edu.cn
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论文类型: 期刊论文
第一作者: Liang, Wenxin
通讯作者: Liang, WX (reprint author), Dalian Univ Technol, Sch Software, 321 Tuqiang St, Dalian 116620, Peoples R China.
合写作者: Li, Xiao,He, Xiaosong,Liu, Xinyue,Zhang, Xianchao
发表时间: 2018-05-01
发表刊物: APPLIED INTELLIGENCE
收录刊物: SCIE、Scopus
卷号: 48
期号: 5,SI
页面范围: 1111-1127
ISSN号: 0924-669X
关键字: Relationship prediction; Ranking strategy; Meta path; Heterogeneous information networks
摘要: In recent years, relationship prediction in heterogeneous information networks (HINs) has become an active topic. The most essential part of this task is how to effectively represent and utilize the important three kinds of information hidden in connections of the network, namely local structure information (Local-info), global structure information (Global-info) and attribute information (Attr-info). Although all the information indicates different features of the network and influence relationship creation in a complementary way, existing approaches utilize them separately or in a partially combined way. In this article, a novel framework named Supervised Ranking framework (S-Rank) is proposed to tackle this issue. To avoid the class imbalance problem, in S-Rank framework we treat the relationship prediction problem as a ranking task and divide it into three phases. Firstly, a Supervised PageRank strategy (SPR) is proposed to rank the candidate nodes according to Global-info and Attr-info. Secondly, a Meta Path-based Ranking method (MPR) utilizing Local-info is proposed to rank the candidate nodes based on their meta path-based features. Finally, the two ranking scores are linearly integrated into the final ranking result which combines all the Attr-info, Global-info and Local-info together. Experiments on DBLP data demonstrate that the proposed S-Rank framework can effectively take advantage of all the three kinds of information for relationship prediction over HINs and outperforms other well-known baseline approaches.