networksampler 0.9.5

Python package for space-filling sampling of network nodes, designed to improve semi-supervised classification via label propagation.

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NetworkSampler: smart networks sampling

NetworkSampler is a Python package for sampling network nodes using space-filling designs. It works by minimizing the geodesic distance of the sample in the network, and maximizing the coverage over the graph.

This package is particularly helpful with semi-supervised machine learning approaches: with this package you can select a sample of nodes to be manually labeled, to extend the classification all over the network via label propagation. The space-filling sampling strategy ensures that the selected seed nodes are well distributed across the network, leading to more accurate and reliable predictions compared to traditional sampling methods.

For more details check the publication:

del Gobbo E., Fontanella L., Ippoliti L., Di Zio S., Fontanella S., Cucco A. (2026). A space-filling sampling approach for collective classification of social media data. Advances in Data Analysis and Classification. https://doi.org/10.1007/s11634-026-00670-z

• Website (including documentation): https://edgresearch.github.io/pylib_networksampler
• Source: https://github.com/edgresearch/pylib_networksampler
• Bug reports: https://github.com/edgresearch/pylib_networksampler/issues
• Pip Package: https://pypi.org/project/networksampler

Tutorials

A comprehensive tutorial is available in the official documentation.

If you prefer a hands-on approach, you can find a complete Jupyter Notebook illustrating the library's features step-by-step directly in the repository. You can open and run it directly in your browser using Google Colab:

• Or view the source file here: notebooks/basic_tutorial.ipynb

Simple example

Extract a sample that maximizes the coverage of a network:

import networksampler
import networkx as nx

# Generate an adjacency matrix of 1000 nodes full connected
G = nx.gaussian_random_partition_graph(1000, 500, 100, 0.20, 0.1)
A = nx.adjacency_matrix(G).todense()

# Extract the sample:
# nodes number = 10
# p = -4 and q = 4
# r = 0.1
result = networksampler.sa_sampling(A, 10, -4, 4, 0.1)
print(result)
# (array([ 14,  135, 213, 256, 345, 560, 678, 690, 900, 967]),
# np.float64(7.727146854012883))

Install

Install the latest version of NetworkSampler:

    $ pip install networksampler

Bugs

Please report any bugs that you find here. Or, even better, fork the repository on GitHub and create a pull request (PR). We welcome all changes, big or small, to improve the library performance.

How to cite this package in publications?

If you use this package in your research, please cite the following paper:

del Gobbo E., Fontanella L., Ippoliti L., Di Zio S., Fontanella S., Cucco A. (2026). A space-filling sampling approach for collective classification of social media data. Advances in Data Analysis and Classification. https://doi.org/10.1007/s11634-026-00670-z

Here you can find the citation in BibTeX format:

@article{delgobbo2026spacefilling,
  author    = {del Gobbo, Emiliano and Fontanella, Lara and Ippoliti, Luigi and Di Zio, Simone and Fontanella, Sara and Cucco, Alex},
  title     = {A space-filling sampling approach for collective classification of social media data},
  journal   = {Advances in Data Analysis and Classification},
  year      = {2026},
  doi       = {10.1007/s11634-026-00670-z},
  url       = {https://doi.org/10.1007/s11634-026-00670-z}
}

Here the citation in RIS format:

TY  - JOUR
AU  - del Gobbo, Emiliano
AU  - Fontanella, Lara
AU  - Ippoliti, Luigi
AU  - Di Zio, Simone
AU  - Fontanella, Sara
AU  - Cucco, Alex
PY  - 2026
TI  - A space-filling sampling approach for collective classification of social media data
T2  - Advances in Data Analysis and Classification
DO  - 10.1007/s11634-026-00670-z
UR  - https://doi.org/10.1007/s11634-026-00670-z
ER  - 

License

The library is released under the GNU LESSER GENERAL PUBLIC LICENSE v3 (see LICENSE).

The artworks (library logo) have Proprietary license and cannot be used for other projects. If you fork the project, you MUST indicate it is a derivate project and not use the logo to identify the project (see artworks/LICENSE.rst).

Copyright (C) 2021-2026 Emiliano del Gobbo

Emiliano del Gobbo [e-mail]