HSNR: A Network Representation Learning Algorithm Using Hierarchical Structure Embedding

YE Zhonglin; ZHAO Haixing; ZHU Yu; XIAO Yuzhi

doi:10.1049/cje.2020.10.001

Volume 29 Issue 6

Dec. 2020

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Article Navigation > Chinese Journal of Electronics > 2020 > 29(6): 1141-1152

YE Zhonglin, ZHAO Haixing, ZHU Yu, et al., “HSNR: A Network Representation Learning Algorithm Using Hierarchical Structure Embedding,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1141-1152, 2020, doi: 10.1049/cje.2020.10.001

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YE Zhonglin, ZHAO Haixing, ZHU Yu, et al., “HSNR: A Network Representation Learning Algorithm Using Hierarchical Structure Embedding,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1141-1152, 2020, doi: 10.1049/cje.2020.10.001

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HSNR: A Network Representation Learning Algorithm Using Hierarchical Structure Embedding

doi: 10.1049/cje.2020.10.001

YE Zhonglin^1,2,3,
ZHAO Haixing^{1,2,3
,},
ZHU Yu^1,2,3,
XIAO Yuzhi^1,2,3

1.
College of Computer, Qinghai Normal University, Xining 810008, China;
2.
Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province, Xining 810008, China;
3.
Key Laboratory of Tibetan Information Processing, Ministry of Education, Xining 810008, China

Funds: This work is supported by the National Natural Science Foundation of China (No.11661069, No.61763041, No.61663041) and Key Research and Transformation Project in Qinghai Province (No.2020-GX-112).

More Information

Corresponding author: ZHAO Haixing (corresponding author) was born in 1969. He received his Doctor of Engineering Degree from the School of Computer Science in Northwestern Polytechnical University in 2004. He also received his Doctor of Science Degree from TWENTE University in Holland. He is a professor and part-time professor at Qinghai Normal University and Shaanxi Normal University, respectively. He is a director of Changjiang Scholars and Innovative Research Team in University. He is also the syndic of Operations Research Society, Combinatorics and Graph Theory Society in China. His research interests include complex network, semantic network and machine translation, hypergraph theory and database, network reliability etc. (Email:h.x.zhao@163.com)
Received Date: 2019-07-19
Publish Date: 2020-12-25

Abstract

Abstract

Vertices in the same group tend to connect densely, and usually share common attributes. Groups of different sizes reflect the relations of vertices in different ranges, and also reflect the features of different orders of the network. In this work, we propose a novel network representation learning algorithm by introducing group features of vertices of different orders to learn more discriminative network representations, named as network representation learning algorithm using Hierarchical structure embedding (HSNR). HSNR algorithm firstly constructs hierarchical relations of network structures of different orders based on greedy algorithm and modularity. In order to introduce hierarchical features into the network representation learning model, HSNR algorithm then introduces the idea of multi-relational modeling from knowledge representation, and converts the hierarchical relations into the triplet form between vertices. Finally, HSNR proposes a joint learning model embedding vertex triplets into the network representations. The experimental results show that the HSRN algorithm presented has an excellent performance in network vertex classification task on three real-world datasets.
- Network representation learning,
- Network embedding,
- Network vertex embedding,
- Network data mining,
- Network feature encoding

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References(46)

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