计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 279-284.doi: 10.11896/jsjkx.191000199

• 计算机网络 • 上一篇    下一篇

共引增强有向网络嵌入研究

吴勇, 王斌君, 翟一鸣, 仝鑫   

  1. 中国人民公安大学警务信息工程与网络安全学院 北京 100240
  • 收稿日期:2019-10-30 修回日期:2020-06-28 发布日期:2020-12-17
  • 通讯作者: 王斌君(wangbinjun@ppsuc.edu.cn)
  • 作者简介:wyppsuc@hotmail.com
  • 基金资助:
    公安部科技强警基础专项(2018GABJC03);中国人民公安大学拔尖人才培养专项资助研究生科研创新项目(2019bsky002)

Study on Co-citation Enhancing Directed Network Embedding

WU Yong, WANG Bin-jun, ZHAI Yi-ming, TONG Xin   

  1. College of Police Information Engineering and Cyber Security People's Public Security University of ,China Beijing 100240,China
  • Received:2019-10-30 Revised:2020-06-28 Published:2020-12-17
  • About author:WU Yong,born in 1989Ph.D candidateis a student member of China Computer Federation.His main research interests include knowledge graph and network representation learning.
    WANG Bin-jun,born in 1962Ph.DprofessorPh.D supervisoris a member of China Computer Federation.His main research interests include know-ledge graphdata mining and natural language processing.
  • Supported by:
    Science and Technology Strengthening Police Basic Program of Ministry of Public Security(2018GABJC03) and Top Talent Training Special Funding Graduate Research and Innovation Project of People’s Public Security University of China(2019bsky002).

摘要: 网络嵌入旨在将网络节点嵌入到一个低维向量空间且最大程度地保存原有网络的拓扑结构及其属性.相比无向网络有向网络具有特殊的非对称传递性可体现在节点之间的高阶相似度量中如何较好地保存这一特性是当前有向网络嵌入研究的热点和难点.针对此问题通过引入有向网络的共引网络设计了共引信息的度量函数给出了一种有向网络高阶相似度量指标融合共引信息的统一框架提出了可以保存非对称传递性的共引增强的高阶相似保存网络嵌入模型(Co-Citation Enhancing High-Order Proximity preserved EmbeddingCCE-HOPE).在4个真实数据集上进行链路预测实验的结果表明不同高阶相似度量指标下不同比重共引信息对效果影响具有一般规律因此可以给出比重的最佳取值范围;在此范围内与现有方法相比CCE-HOPE方法可有效提高链接预测的准确度.

关键词: 非对称传递, 共引网络, 链路预测, 有向网络嵌入

Abstract: Network embedding algorithms embed a network into a low-dimensional vector space where the structure and the inherent properties of the graph can be preserved to the greatest extent.Compared with undirected networksdirected networks have special asymmetric transitivity which can be reflected in the high-order similarity measurement between nodes.A hot spot and difficulty of current directed network embedding research is how to preserve this feature well.Aiming at this problemthis paper introduces the co-citation network of directed networks and designs a metric function of the co-introduction information.At the same timea unified framework is created for fusing the co-citation information and the high-order similarity metrics of directed networks.Thenthis paper proposes a co-citation enhancing high-order proximity preserved embedding methodcalled CCE-HOPEwhich can preserve the asymmetric transitivity well.In experimentsthe proposed model is evaluated on link prediction using four real data sets.The results show that under different high-order similarity metricsthe performance of different proportions of co-introduction information follows a general regularityso the optimal range of the proportion can be determined.Compared with other state-of-the-art methodsthe method can effectively improve the accuracy of link prediction when the proportion of co-introduction information is within the optimal range.

Key words: Asymmetric transitivity, Co-citation network, Directed network embedding, Link prediction

中图分类号: 

  • TP391
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