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Community detection in hypernetwork via Density-Ordered Tree partitionAuthor-Name: Cheng, Qing

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  • Liu, Zhong
  • Huang, Jincai
  • Cheng, Guangquan

Abstract

Hypernetwork, as a useful representation of natural and social systems has received increasing interests from researchers. Community is crucial to understand the structural and functional properties of the hypernetworks. Here, we propose a new method to uncover the communities of hypernetworks. We construct a Density-Ordered Tree (DOT) to represent original data by combining density and distance, and the community detection in hypernetwork is converted to a DOT partition problem. Then, an anomaly detection strategy using box-plot rule is applied to partition DOT and judge whether there is a significant community structure in the hypernetwork. Moreover, visual inspection as a complementary approach of box-plot rule can effectively improve the effectiveness of community detection. Finally, the method is compared with existing methods in both synthetic and real-world networks.

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  • Liu, Zhong & Huang, Jincai & Cheng, Guangquan, 2016. "Community detection in hypernetwork via Density-Ordered Tree partitionAuthor-Name: Cheng, Qing," Applied Mathematics and Computation, Elsevier, vol. 276(C), pages 384-393.
    • Handle: RePEc:eee:apmaco:v:276:y:2016:i:c:p:384-393
    DOI: 10.1016/j.amc.2015.12.039
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    References listed on IDEAS

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    1. Estrada, Ernesto & Rodríguez-Velázquez, Juan A., 2006. "Subgraph centrality and clustering in complex hyper-networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 581-594.
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    3. Jian-Wei Wang & Li-Li Rong & Qiu-Hong Deng & Ji-Yong Zhang, 2010. "Evolving hypernetwork model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 493-498, October.
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    Cited by:

    1. Feng Wang & Feng Hu & Rumeng Chen & Naixue Xiong, 2023. "HLEGF: An Effective Hypernetwork Community Detection Algorithm Based on Local Expansion and Global Fusion," Mathematics, MDPI, vol. 11(16), pages 1-17, August.
    2. Hanlin You & Mengjun Li & Jiang Jiang & Bingfeng Ge & Xueting Zhang, 2017. "Evolution monitoring for innovation sources using patent cluster analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 693-715, May.
    3. Yu, Ping & Wang, Zhiping & Wang, Peiwen & Yin, Haofei & Wang, Jia, 2022. "Dynamic evolution of shipping network based on hypergraph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    4. Yu Wei & Sun Ning, 2018. "Establishment and Analysis of the Supernetwork Model for Nanjing Metro Transportation System," Complexity, Hindawi, vol. 2018, pages 1-11, December.
    5. Jie Zhang & Pengpeng Yao & Hochung Wu & John H. Xin, 2023. "Automatic color pattern recognition of multispectral printed fabric images," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2747-2763, August.
    6. Cheng, Qing & Lu, Xin & Liu, Zhong & Huang, Jincai & Cheng, Guangquan, 2016. "Spatial clustering with Density-Ordered tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 188-200.
    7. Jiang, Zhongzhou & Liu, Jing & Wang, Shuai, 2016. "Traveling salesman problems with PageRank Distance on complex networks reveal community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 293-302.
    8. Bo Zhang & Yifei Mi & Lele Zhang & Yuping Zhang & Maozhen Li & Qianqian Zhai & Meizi Li, 2022. "Dynamic Community Detection Method of a Social Network Based on Node Embedding Representation," Mathematics, MDPI, vol. 10(24), pages 1-22, December.

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