Bipartite mixed membership distribution-free model. A novel model for community detection in overlapping bipartite weighted networks
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866914741324087296 |
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| author | Qing, Huan Wang, Jingli |
| author_facet | Qing, Huan Wang, Jingli |
| contents | Modeling and estimating mixed memberships for overlapping unipartite un-weighted networks has been well studied in recent years. However, to our knowledge, there is no model for a more general case, the overlapping bipartite weighted networks. To close this gap, we introduce a novel model, the Bipartite Mixed Membership Distribution-Free (BiMMDF) model. Our model allows an adjacency matrix to follow any distribution as long as its expectation has a block structure related to node membership. In particular, BiMMDF can model overlapping bipartite signed networks and it is an extension of many previous models, including the popular mixed membership stochastic blcokmodels. An efficient algorithm with a theoretical guarantee of consistent estimation is applied to fit BiMMDF. We then obtain the separation conditions of BiMMDF for different distributions. Furthermore, we also consider missing edges for sparse networks. The advantage of BiMMDF is demonstrated in extensive synthetic networks and eight real-world networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_00912 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Bipartite mixed membership distribution-free model. A novel model for community detection in overlapping bipartite weighted networks Qing, Huan Wang, Jingli Social and Information Networks Data Analysis, Statistics and Probability Machine Learning Modeling and estimating mixed memberships for overlapping unipartite un-weighted networks has been well studied in recent years. However, to our knowledge, there is no model for a more general case, the overlapping bipartite weighted networks. To close this gap, we introduce a novel model, the Bipartite Mixed Membership Distribution-Free (BiMMDF) model. Our model allows an adjacency matrix to follow any distribution as long as its expectation has a block structure related to node membership. In particular, BiMMDF can model overlapping bipartite signed networks and it is an extension of many previous models, including the popular mixed membership stochastic blcokmodels. An efficient algorithm with a theoretical guarantee of consistent estimation is applied to fit BiMMDF. We then obtain the separation conditions of BiMMDF for different distributions. Furthermore, we also consider missing edges for sparse networks. The advantage of BiMMDF is demonstrated in extensive synthetic networks and eight real-world networks. |
| title | Bipartite mixed membership distribution-free model. A novel model for community detection in overlapping bipartite weighted networks |
| topic | Social and Information Networks Data Analysis, Statistics and Probability Machine Learning |
| url | https://arxiv.org/abs/2211.00912 |