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A Semantic Community Detection Algorithm Based on Quantizing Progress

Author

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  • Xu Han
  • Deyun Chen
  • Hailu Yang

Abstract

The semantic social network is a kind of network that contains enormous nodes and complex semantic information, and the traditional community detection algorithms could not give the ideal cogent communities instead. To solve the issue of detecting semantic social network, we present a clustering community detection algorithm based on the PSO-LDA model. As the semantic model is LDA model, we use the Gibbs sampling method that can make quantitative parameters map from semantic information to semantic space. Then, we present a PSO strategy with the semantic relation to solve the overlapping community detection. Finally, we establish semantic modularity (SimQ) for evaluating the detected semantic communities. The validity and feasibility of the PSO-LDA model and the semantic modularity are verified by experimental analysis.

Suggested Citation

  • Xu Han & Deyun Chen & Hailu Yang, 2019. "A Semantic Community Detection Algorithm Based on Quantizing Progress," Complexity, Hindawi, vol. 2019, pages 1-13, January.
    • Handle: RePEc:hin:complx:3475458
    DOI: 10.1155/2019/3475458
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    References listed on IDEAS

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    1. Ulzii-Utas Narantsatsralt & Sanggil Kang, 2017. "Social Network Community Detection Using Agglomerative Spectral Clustering," Complexity, Hindawi, vol. 2017, pages 1-10, November.
    2. Shen, Huawei & Cheng, Xueqi & Cai, Kai & Hu, Mao-Bin, 2009. "Detect overlapping and hierarchical community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1706-1712.
    3. Yair Neuman & Yiftach Neuman & Yochai Cohen, 2017. "A Novel Procedure for Measuring Semantic Synergy," Complexity, Hindawi, vol. 2017, pages 1-8, January.
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