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A network representation method based on edge information extraction

  • Wei Fan
  • Hui Min Wang
  • Yan XingEmail author
  • Rui Huang
  • W. H. Ip
  • Kai Leung Yung
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Abstract

In recent years, network representation learning has attracted extensive attention in the academic field due to its significant application potential. However, most of the methods cannot explore edge information in the network deeply, resulting in poor performance at downstream tasks such as classification, clustering and link prediction. In order to solve this problem, we propose a novel way to extract network information. First, the original network is transformed into an edge network with structure and edge information. Then, edge representation vectors can be obtained directly by using an existing network representation model with edge network as its input. Node representation vectors can also be obtained by utilizing the relationships between edges and nodes. Compared with the structure of original network, the edge network is denser, which can help solving the problems caused by sparseness. Extensive experiments on several real-world networks demonstrate that edge network outperforms original network in various graph mining tasks, i.e., node classification and node clustering.

Keywords

Network representation learning Edge network Node representation vectors Edge representation vectors 

Notes

Acknowledgements

This work is partially supported by Grants from the National Natural Science Foundation of China (U1333109), Fundamental Research Funds for the Central Universities of Civil Aviation University of China (3122018C020, 3122018C021), Scientific Research Foundation of Civil Aviation University of China (600/600001050115, 600/600001050117) and a fund from the Hong Kong Polytechnic University, Department of Industrial and Systems Engineering (H-ZG3K).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Wei Fan
    • 1
  • Hui Min Wang
    • 1
  • Yan Xing
    • 1
    Email author
  • Rui Huang
    • 1
  • W. H. Ip
    • 2
    • 3
  • Kai Leung Yung
    • 4
  1. 1.Department of Computer Science and TechnologyCivil Aviation University of ChinaTianjinChina
  2. 2.Department of Industrial and Systems EngineeringHong Kong Polytechnic UniversityKowloonChina
  3. 3.Department of Mechanical EngineeringUniversity of SaskatchewanSaskatoonCanada
  4. 4.Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHung HomHong Kong

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