Abstract
Recently, social networks play significant roles in real-time content analysis. Many studies have investigated social networks by analysis of contents based on users. However, these studies have some limitations of social network analysis while clustering contents based on users’ behavior. The paper has presented the Hybrid Louvain-Clustering model using a knowledge graph to cluster contents based on user behaviors in a social network. In the proposed model, all multi-dimensional user relationships represent in a knowledge graph while clustering contents based on user behaviors in real-time on social networks. The experiment demonstrated that the proposed method for a large-scale social network and reached efficient clustering content based on user’s behaviors. Additionally, experimental results show that the proposed algorithm enhances the performance of clustering contents based on user’s behavior of a social network analysis.
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This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.05-2019.316.
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Van Pham, H., Tien, D.N. (2021). Hybrid Louvain-Clustering Model Using Knowledge Graph for Improvement of Clustering User’s Behavior on Social Networks. In: Tran, DT., Jeon, G., Nguyen, T.D.L., Lu, J., Xuan, TD. (eds) Intelligent Systems and Networks . ICISN 2021. Lecture Notes in Networks and Systems, vol 243. Springer, Singapore. https://doi.org/10.1007/978-981-16-2094-2_16
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DOI: https://doi.org/10.1007/978-981-16-2094-2_16
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