Persistent Homology Combined with Machine Learning for Social Network Activity Analysis
Currently, the rapid development of social media enables people to communicate more and more frequently in the network. Classifying user activities in social networks helps to better understand user behavior in social networks. This paper first creates an ego network for each user, encodes the highe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/1/19 |
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| author | Zhijian Zhang Yuqing Sun Yayun Liu Lin Jiang Zhengmi Li |
| author_facet | Zhijian Zhang Yuqing Sun Yayun Liu Lin Jiang Zhengmi Li |
| author_sort | Zhijian Zhang |
| collection | DOAJ |
| description | Currently, the rapid development of social media enables people to communicate more and more frequently in the network. Classifying user activities in social networks helps to better understand user behavior in social networks. This paper first creates an ego network for each user, encodes the higher-order topological features of the ego network as persistence diagrams using persistence homology, and computes the persistence entropy. Then, based on the persistence entropy, this paper defines the Norm Entropy-NE(X) to represent the complexity of the topological features of the ego network, a larger NE(X) indicates a higher topological complexity, i.e., the higher the activity of the nodes, thus indicating the degree of activity of the nodes. The paper uses the extracted set of feature vectors to train the machine learning model to classify the users in the social network. Numerical experiments are conducted to evaluate the performance of clustering quality metrics such as profile coefficients. The results show that the proposed algorithm can effectively classify social network users into different groups, which provides a good foundation for further research and application. |
| format | Article |
| id | doaj-art-a5cdf9d4e791432e97372dc33905789f |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-a5cdf9d4e791432e97372dc33905789f2025-01-24T13:31:41ZengMDPI AGEntropy1099-43002024-12-012711910.3390/e27010019Persistent Homology Combined with Machine Learning for Social Network Activity AnalysisZhijian Zhang0Yuqing Sun1Yayun Liu2Lin Jiang3Zhengmi Li4Faculty of Science, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Science, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Science, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Science, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Science, Kunming University of Science and Technology, Kunming 650500, ChinaCurrently, the rapid development of social media enables people to communicate more and more frequently in the network. Classifying user activities in social networks helps to better understand user behavior in social networks. This paper first creates an ego network for each user, encodes the higher-order topological features of the ego network as persistence diagrams using persistence homology, and computes the persistence entropy. Then, based on the persistence entropy, this paper defines the Norm Entropy-NE(X) to represent the complexity of the topological features of the ego network, a larger NE(X) indicates a higher topological complexity, i.e., the higher the activity of the nodes, thus indicating the degree of activity of the nodes. The paper uses the extracted set of feature vectors to train the machine learning model to classify the users in the social network. Numerical experiments are conducted to evaluate the performance of clustering quality metrics such as profile coefficients. The results show that the proposed algorithm can effectively classify social network users into different groups, which provides a good foundation for further research and application.https://www.mdpi.com/1099-4300/27/1/19social networkspersistent homologymachine learningpersistent entropyclustering |
| spellingShingle | Zhijian Zhang Yuqing Sun Yayun Liu Lin Jiang Zhengmi Li Persistent Homology Combined with Machine Learning for Social Network Activity Analysis Entropy social networks persistent homology machine learning persistent entropy clustering |
| title | Persistent Homology Combined with Machine Learning for Social Network Activity Analysis |
| title_full | Persistent Homology Combined with Machine Learning for Social Network Activity Analysis |
| title_fullStr | Persistent Homology Combined with Machine Learning for Social Network Activity Analysis |
| title_full_unstemmed | Persistent Homology Combined with Machine Learning for Social Network Activity Analysis |
| title_short | Persistent Homology Combined with Machine Learning for Social Network Activity Analysis |
| title_sort | persistent homology combined with machine learning for social network activity analysis |
| topic | social networks persistent homology machine learning persistent entropy clustering |
| url | https://www.mdpi.com/1099-4300/27/1/19 |
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