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ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks

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Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2012))

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Abstract

Community detection is essential for identifying cohesive groups in complex networks. Artificial benchmarks are critical for evaluating community detection algorithms, offering controlled environments with known community structures. However, existing benchmarks mainly focus on homogeneous networks and overlook the unique characteristics of heterogeneous networks. This paper proposes a novel artificial benchmark, called ABCD-HN (Artificial Network Benchmark for Community Detection on Heterogeneous Networks), for community detection in heterogeneous networks. This benchmark enables the generation of artificial heterogeneous networks with controllable community quantity, node quantity, and community complexity. Additionally, an evaluation framework for artificial heterogeneous networks is proposed to assess their effectiveness. Experimental results demonstrate the effectiveness and usability of ABCD-HN as a benchmark for artificial heterogeneous networks.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 62002063 and No. U21A20472, the National Key Research and Development Plan of China under Grant No. 2021YFB3600503, the Fujian Collaborative Innovation Center for Big Data Applications in Governments, the Fujian Industry-Academy Cooperation Project under Grant No. 2017H6008 and No. 2018H6010, the Natural Science Foundation of Fujian Province under Grant No. 2022J01118, No. 2020J05112 and No. 2020J01420, the Fujian Provincial Department of Education under Grant No. JAT190026, the Major Science and Technology Project of Fujian Province under Grant No. 2021HZ022007, the Hong Kong RGC TRS T41-603/20R and Haixi Government Big Data Application Cooperative Innovation Center.

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Authors and Affiliations

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

    Junjie Liu, Kun Guo & Ling Wu

  2. College of Computer and Data Science/College of Software, Fuzhou University, Fuzhou, 350108, China

    Junjie Liu, Kun Guo & Ling Wu

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

    Junjie Liu, Kun Guo & Ling Wu

Authors
  1. Junjie Liu
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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. Harbin Engineering University, Harbin, China

    Tong Wang

  4. Tongji University, Shanghai, China

    Hongfei Fan

  5. Guangdong University of Technology, Guangzhou, China

    Dongning Liu

  6. Tongji University, Shanghai, China

    Bowen Du

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Liu, J., Guo, K., Wu, L. (2024). ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_13

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  • DOI: https://doi.org/10.1007/978-981-99-9637-7_13

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  • Print ISBN: 978-981-99-9636-0

  • Online ISBN: 978-981-99-9637-7

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