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PCANE: Preserving Context Attributes for Network Embedding

  • Danhao Zhu
  • Xin-yu DaiEmail author
  • Kaijia Yang
  • Jiajun Chen
  • Yong He
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)

Abstract

Through mapping network nodes into low-dimensional vectors, network embedding methods have shown promising results for many downstream tasks, such as link prediction and node classification. Recently, attributed network embedding obtained progress on the network associated with node attributes. However, it is insufficient to ignore the attributes of the context nodes, which are also helpful for node proximity. In this paper, we propose a new attributed network embedding method named PCANE (Preserving Context Attributes for Network Embedding). PCANE preserves both network structure and the context attributes by optimizing new object functions, and further produces more informative node representations. PCANE++ is also proposed to represent the isolated nodes, and is better to represent high degree nodes. Experiments on 3 real-world attributed networks show that our methods outperform the other network embedding methods on link prediction and node classification tasks.

Notes

Acknowledgement

This work is sponsored, in part, by The Natural Science Foundation of the Jiangsu Higher Education Institutions of China under grant number 18KJB510010 and National Nature Science Foundation of China (NSFC) under grant number 61472183.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Danhao Zhu
    • 1
    • 2
  • Xin-yu Dai
    • 1
    Email author
  • Kaijia Yang
    • 1
  • Jiajun Chen
    • 1
  • Yong He
    • 2
  1. 1.Nanjing UniversityNanjingPeople’s Republic of China
  2. 2.Jiangsu Police InstituteNanjingPeople’s Republic of China

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