ACRONYM: Augmented degree corrected, Community Reticulated Organized Network Yielding Model
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913309966467072 |
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| author | Leinwand, Benjamin Lyzinski, Vince |
| author_facet | Leinwand, Benjamin Lyzinski, Vince |
| contents | Modeling networks can serve as a means of summarizing high-dimensional complex systems. Adapting an approach devised for dense, weighted networks, we propose a new method for generating and estimating unweighted networks. This approach can describe a broader class of potential networks than existing models, including those where nodes in different subnetworks connect to one another via various attachment mechanisms, inducing flexible and varied community structures. While unweighted edges provide less resolution than continuous weights, restricting to the binary case permits the use of likelihood-based estimation techniques, which can improve estimation of nodal features. The extra flexibility may contribute a different understanding of network generating structures, particularly for networks with heterogeneous densities in different regions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07462 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ACRONYM: Augmented degree corrected, Community Reticulated Organized Network Yielding Model Leinwand, Benjamin Lyzinski, Vince Physics and Society Applications Modeling networks can serve as a means of summarizing high-dimensional complex systems. Adapting an approach devised for dense, weighted networks, we propose a new method for generating and estimating unweighted networks. This approach can describe a broader class of potential networks than existing models, including those where nodes in different subnetworks connect to one another via various attachment mechanisms, inducing flexible and varied community structures. While unweighted edges provide less resolution than continuous weights, restricting to the binary case permits the use of likelihood-based estimation techniques, which can improve estimation of nodal features. The extra flexibility may contribute a different understanding of network generating structures, particularly for networks with heterogeneous densities in different regions. |
| title | ACRONYM: Augmented degree corrected, Community Reticulated Organized Network Yielding Model |
| topic | Physics and Society Applications |
| url | https://arxiv.org/abs/2404.07462 |