A Semantic Community Detection Algorithm Based on Quantizing Progress

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 com...

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Bibliographic Details
Published inComplexity (New York, N.Y.) Vol. 2019; no. 2019; pp. 1 - 13
Main Authors Han, Xu, Yang, Hailu, Deyun, Chen
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2019
Hindawi
Hindawi Limited
Wiley
Subjects
Algorithms
Clustering
Community
Counting
Feasibility studies
Information retrieval
International conferences
Knowledge management
Modularity
Parameter estimation
Researchers
Semantics
Social networks
Text analysis
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Summary: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.
ISSN:1076-2787
1099-0526
DOI:10.1155/2019/3475458