GroupMultiNeSS 0.0.3
GroupMultiNeSS package
Keywords: python, multiplex networks, multiness, latent space models

# 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

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