Metadata-Version: 2.4
Name: GroupMultiNeSS
Version: 0.0.3
Summary: GroupMultiNeSS package
Author: Alexander Kagan
Author-email: <amkagan@umich.edu>
Keywords: python,multiplex networks,multiness,latent space models
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy<2
Requires-Dist: scipy
Requires-Dist: typing
Requires-Dist: joblib
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: statsmodels
Requires-Dist: scikit-learn
Requires-Dist: more_itertools
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: keywords
Dynamic: license-file
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Dynamic: summary

# GroupMultiNeSS

GroupMultiNeSS is a package for statistical modeling of multilayer networks. It implements multiple approaches allowing to extract shared, group, and individual latent structures from a collection of networks on a shared set of nodes. Specifically, it contains the implementation of fitting sampling procedures for the following models:
- GroupMultiNeSS [Kagan et al. (2025)] - likelihood based approach with nuclear norm penalization, accounts for the additional group latent structure
- MultiNeSS [[MacDonald et al. (2021)](https://arxiv.org/abs/2012.14409)] - likelihood based approach with nuclear norm penalization
- MultiNeSS [[Tian et al. (2024)](https://arxiv.org/abs/2412.02151)] - likelihood based approach with pre-estimation of latent ranks via Shared Space Hunting algorithm
- COSIE ([Arroyo et al.](https://arxiv.org/abs/1906.10026)) - spectral-based Multiple Adjacency Spectral Embedding algorithm

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install GroupMultiNeSS.

```bash
pip install GroupMultiNeSS
```

## Usage

```python
# Imports
import numpy as np
from GroupMultiNeSS.group_multiness import GroupMultiNeSS
from GroupMultiNeSS.data_generation import GroupLatentPositionGenerator
from GroupMultiNeSS.utils import make_group_indices


# Sample true latent positions and group indices
n, M, K = 200, 16, 4
group_props = np.ones(K) / K  # make ballanced groups
group_indices = make_group_indices(group_props, M)

lpg = GroupLatentPositionGenerator(n_nodes=n, n_layers=M, group_indices=group_indices)
lpg.generate(random_seed=1)

As, Ps_true, S_true, Qs_true, Rs_true = lpg.As, lpg.Ps, lpg.S, lpg.Qs, lpg.Rs

# Fit GroupMultiNeSS model and compute the relative errors with ground-truth
group_multiness = GroupMultiNeSS(group_indices, n_jobs=K)
group_multiness.fit(As, lr=0.8)
print(group_multiness.make_final_error_report(S_true, Qs_true, Rs_true, Ps=Ps_true))

# {'Shared component': 0.026, 'Group components': 0.051, 'Individual components': 0.122, 'Ps': 0.077}
```


## License

MIT License

Copyright (c) 2024 Alexander Kagan

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.