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张宪超
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主要任职: 国防(先进)科学技术发展研究院副院长

性别: 男

毕业院校: 中国科技大学

学位: 博士

在职信息:在职

所在单位: 软件学院

学科: 计算机应用技术 软件工程

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Supervised ranking framework for relationship prediction in heterogeneous information networks

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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.

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