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Attributed Network Embedding Based on Attributed-Subgraph-Based Random Walk for Community Detection

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Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1492))

Abstract

The random-walk-based attribute network embedding methods aim to learn a low-dimensional embedding vector for each node considering the network structure and node attributes, facilitating various downstream inference tasks. However, most existing attribute network embedding methods base on random walk usually sample many redundant samples and suffer from inconsistency between node structure and attributes. In this paper, we propose a novel attributed network embedding method for community detection, which can generate node sequences based on attributed-subgraph-based random walk and filter redundant samples before model training. In addition, an improved network embedding enhancement strategy is applied to integrate high-order attributed and structure information of nodes into embedding vectors. Experimental results of community detection on synthetic network and real-world network show that our algorithm is effective and efficient compared with other algorithms.

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Notes

  1. 1.

    https://linqs.soe.ucsc.edu/data.

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Author information

Authors and Affiliations

  1. Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350108, China

    Qinze Wang, Kun Guo & Ling Wu

  2. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350108, China

    Qinze Wang, Kun Guo & Ling Wu

  3. Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou, 350108, China

    Qinze Wang, Kun Guo & Ling Wu

Authors
  1. Qinze Wang
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  2. Kun Guo
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  3. Ling Wu
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Corresponding author

Correspondence to Kun Guo .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Hunan University of Science and Technology, Xiangtan, China

    Buqing Cao

  4. Tongji University, Shanghai, China

    Hongfei Fan

  5. Guangdong University of Technology, Guangzhou, China

    Dongning Liu

  6. University of Warwick, Coventry, UK

    Bowen Du

  7. University of Shanghai for Science and Technology, Shanghai, China

    Liping Gao

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Wang, Q., Guo, K., Wu, L. (2022). Attributed Network Embedding Based on Attributed-Subgraph-Based Random Walk for Community Detection. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1492. Springer, Singapore. https://doi.org/10.1007/978-981-19-4549-6_16

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  • DOI: https://doi.org/10.1007/978-981-19-4549-6_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-4548-9

  • Online ISBN: 978-981-19-4549-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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