mlsanityData to 13 Oct 2026

Bipartite mixed membership distribution-free model. A novel model for community detection in overlapping bipartite weighted networks

arXiv 2211.00912 ↗First posted 2 Nov 2022cs.SI physics.data-an stat.MLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 37 of its 121 references that are in the map. First posted on arXiv 2 Nov 2022.

Builds on

37 of 121 references.

References · 37

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25 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2024Community detection by spectral methods in multi-layer networkscited by 3
  2. 2023Mixed membership estimation for categorical data with weighted responsescited by 2
  3. 2025Multilayer Artificial Benchmark for Community Detection (mABCD)cited by 1
  4. 2024Estimating mixed memberships in multi-layer networkscited by 0