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A network representation method based on edge information extraction

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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.

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Notes

  1. http://www.orgnet.com/.

  2. http://www-personal.umich.edu/~mejn/netdata/.

  3. http://linqs.cs.umd.edu/projects/projects/lbc.

  4. http://www.weizmann.ac.il/mcb/UriAlon/e-coli-transcription-network.

  5. http://moreno.ss.uci.edu/data.html#Pro-pro.

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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).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Civil Aviation University of China, Tianjin, China

    Wei Fan, Hui Min Wang, Yan Xing & Rui Huang

  2. Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Kowloon, China

    W. H. Ip

  3. Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada

    W. H. Ip

  4. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Kai Leung Yung

Authors
  1. Wei Fan
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  2. Hui Min Wang
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  3. Yan Xing
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  4. Rui Huang
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  5. W. H. Ip
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  6. Kai Leung Yung
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Correspondence to Yan Xing.

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Communicated by Mu-Yen Chen.

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Fan, W., Wang, H.M., Xing, Y. et al. A network representation method based on edge information extraction. Soft Comput 24, 8223–8231 (2020). https://doi.org/10.1007/s00500-019-04451-z

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