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Community detection in complex networks using density-based clustering algorithm and manifold learning

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  • You, Tao
  • Cheng, Hui-Min
  • Ning, Yi-Zi
  • Shia, Ben-Chang
  • Zhang, Zhong-Yuan

Abstract

Like clustering analysis, community detection aims at assigning nodes in a network into different communities. Fdp is a recently proposed density-based clustering algorithm which does not need the number of clusters as prior input and the result is insensitive to its parameter. However, Fdp cannot be directly applied to community detection due to its inability to recognize the community centers in the network. To solve the problem, a new community detection method (named IsoFdp) is proposed in this paper. First, we use IsoMap technique to map the network data into a low dimensional manifold which can reveal diverse pair-wised similarity. Then Fdp is applied to detect the communities in the network. An improved partition density function is proposed to select the proper number of communities automatically. We test our method on both synthetic and real-world networks, and the results demonstrate the effectiveness of our algorithm over the state-of-the-art methods.

Suggested Citation

  • You, Tao & Cheng, Hui-Min & Ning, Yi-Zi & Shia, Ben-Chang & Zhang, Zhong-Yuan, 2016. "Community detection in complex networks using density-based clustering algorithm and manifold learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 221-230.
    • Handle: RePEc:eee:phsmap:v:464:y:2016:i:c:p:221-230
    DOI: 10.1016/j.physa.2016.07.025
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    References listed on IDEAS

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    Cited by:

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    2. Jianjun Cheng & Xing Su & Haijuan Yang & Longjie Li & Jingming Zhang & Shiyan Zhao & Xiaoyun Chen, 2019. "Neighbor Similarity Based Agglomerative Method for Community Detection in Networks," Complexity, Hindawi, vol. 2019, pages 1-16, May.
    3. Zhang, Weitong & Zhang, Rui & Shang, Ronghua & Li, Juanfei & Jiao, Licheng, 2019. "Application of natural computation inspired method in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 130-150.
    4. Ding, Jiajun & He, Xiongxiong & Yuan, Junqing & Chen, Yan & Jiang, Bo, 2018. "Community detection by propagating the label of center," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 675-686.
    5. Li, Yafang & Jia, Caiyan & Li, Jianqiang & Wang, Xiaoyang & Yu, Jian, 2018. "Enhanced semi-supervised community detection with active node and link selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 219-232.
    6. Sun, Jun-yan & Tang, Jian-ming & Fu, Wei-ping & Wu, Bing-ying, 2017. "Hybrid modeling and empirical analysis of automobile supply chain network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 377-389.
    7. Huang, Yan & Wan, Jiansong & Huang, Xin, 2019. "Quantitative analysis of financial system fragility based on manifold curvature," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1276-1285.

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