A Semantic Community Detection Algorithm Based on Quantizing Progress. (9th January 2019)

• Record Type: Journal Article
• Authors: Han, Xu; Chen, Deyun; Yang, Hailu
• Other Names: De Meo Pasquale Academic Editor.
• Is Part Of: Complexity. Volume 2019(2019)
• Publication Date: 2019-01-09
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2019/3475458
• Languages: English
• ISSNs: 1076-2787

Abstract

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.