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A Semantic Community Detection Algorithm Based on Quantizing Progress
Joint Authors
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-09
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Han, Xu& Deyun, Chen& Yang, Hailu. 2019. A Semantic Community Detection Algorithm Based on Quantizing Progress. Complexity،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1131420
Modern Language Association (MLA)
Han, Xu…[et al.]. A Semantic Community Detection Algorithm Based on Quantizing Progress. Complexity No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1131420
American Medical Association (AMA)
Han, Xu& Deyun, Chen& Yang, Hailu. A Semantic Community Detection Algorithm Based on Quantizing Progress. Complexity. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1131420
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131420