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Heterogeneous-attributes enhancement deep framework for network embedding

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Abstract

Network embedding, which targets at learning the vector representation of vertices, has become a crucial issue in network analysis. However, considering the complex structures and heterogeneous attributes in real-world networks, existing methods may fail to handle the inconsistencies between the structure topology and attribute proximity. Thus, more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more information. To that end, in this paper, we propose a heterogeneous-attributes enhancement deep framework (HEDF), which could better capture the non-linear structure and associated information in a deep learning way, and effectively combine the structure information of multi-views by the combining layer. Along this line, the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised mode. The extensive validations on several real-world datasets show that our model could outperform the baselines, especially for the sparse and inconsistent situation with less training data.

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Acknowledgements

This research was partially supported by the National Natural Science Foundation of China (Grants Nos. U1605251 and 61727809).

Author information

Authors and Affiliations

  1. Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China, Hefei, 230022, China

    Lisheng Qiao, Kai Li & Enhong Chen

  2. National Key Laboratory of Blind Signal Processing, Chengdu, 610041, China

    Fan Zhang

  3. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230022, China

    Xiaohui Huang

Authors
  1. Lisheng Qiao
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  2. Fan Zhang
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  3. Xiaohui Huang
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  4. Kai Li
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  5. Enhong Chen
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Corresponding author

Correspondence to Enhong Chen.

Additional information

Lisheng Qiao received the BE degree in science and technology in Electronic Science and Technology from Southwest Jiao Tong University, China in 2006. He is currently pursuing the PhD degree with the School of Computer Science and Technology, University of Science and Technology of China, China. His current research interest includes social networks, data mining and machine learning.

Fan Zhang received the the BE degree in National Key Laboratory of Blind Signal Processing, China in 2004. She is currently working as a senior engineer at the same laboratory. Her current research interest includes artificial intelligence, data mining and machine learning.

Xiaohui Huang received the BE degree in technology of computer application from University of Information Engineering, China in 2012. He is currently pursuing the PhD degree with the School of Computer Science and Technology, University of Science and Technology of China, China. His current research interest includes data mining, deep learning and neural network.

Kai Li received the BE degree in Computer Science in 2000 from Yanshan University, China and the MS degree of Computer Science from Jilin University, China in 2003. He is currently a PhD student in the School of Computer Science and Technology, University of Science and Technology of China, China. His major research interests include social networks, human dynamics and machine learning.

Enhong Chen received the PhD degree from the University of Science and Technology of China. He is a professor and vice dean of the School of Computer Science, USTC, China. His general area of research includes data mining and machine learning, social network analysis, and recommender systems. He has published more than 100 papers in refereed conferences and journals, including IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Industrial Electronics, KDD, ICDM, NIPS, and CIKM. He was on program committees of numerous conferences including KDD, ICDM, and SDM. He received the Best Application Paper Award on KDD-2008, the Best Research Paper Award on ICDM-2011, and the Best of SDM-2015. His research is supported by the US National Science Foundation for Distinguished Young Scholars of China. He is a senior member of the IEEE.

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Qiao, L., Zhang, F., Huang, X. et al. Heterogeneous-attributes enhancement deep framework for network embedding. Front. Comput. Sci. 15, 156616 (2021). https://doi.org/10.1007/s11704-021-9515-8

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