Two-way Node Popularity Model for Directed and Bipartite Networks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912152019795968 |
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| author | Jing, Bing-Yi Li, Ting Wang, Jiangzhou Wang, Ya |
| author_facet | Jing, Bing-Yi Li, Ting Wang, Jiangzhou Wang, Ya |
| contents | There has been extensive research on community detection in directed and bipartite networks. However, these studies often fail to consider the popularity of nodes in different communities, which is a common phenomenon in real-world networks. To address this issue, we propose a new probabilistic framework called the Two-Way Node Popularity Model (TNPM). The TNPM also accommodates edges from different distributions within a general sub-Gaussian family. We introduce the Delete-One-Method (DOM) for model fitting and community structure identification, and provide a comprehensive theoretical analysis with novel technical skills dealing with sub-Gaussian generalization. Additionally, we propose the Two-Stage Divided Cosine Algorithm (TSDC) to handle large-scale networks more efficiently. Our proposed methods offer multi-folded advantages in terms of estimation accuracy and computational efficiency, as demonstrated through extensive numerical studies. We apply our methods to two real-world applications, uncovering interesting findings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08051 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Two-way Node Popularity Model for Directed and Bipartite Networks Jing, Bing-Yi Li, Ting Wang, Jiangzhou Wang, Ya Methodology Social and Information Networks Statistics Theory Computation Machine Learning There has been extensive research on community detection in directed and bipartite networks. However, these studies often fail to consider the popularity of nodes in different communities, which is a common phenomenon in real-world networks. To address this issue, we propose a new probabilistic framework called the Two-Way Node Popularity Model (TNPM). The TNPM also accommodates edges from different distributions within a general sub-Gaussian family. We introduce the Delete-One-Method (DOM) for model fitting and community structure identification, and provide a comprehensive theoretical analysis with novel technical skills dealing with sub-Gaussian generalization. Additionally, we propose the Two-Stage Divided Cosine Algorithm (TSDC) to handle large-scale networks more efficiently. Our proposed methods offer multi-folded advantages in terms of estimation accuracy and computational efficiency, as demonstrated through extensive numerical studies. We apply our methods to two real-world applications, uncovering interesting findings. |
| title | Two-way Node Popularity Model for Directed and Bipartite Networks |
| topic | Methodology Social and Information Networks Statistics Theory Computation Machine Learning |
| url | https://arxiv.org/abs/2412.08051 |