sknet
sknet: A Python framework for Machine Learning in Complex Networks - Published in JOSS (2021)
Science Score: 93.0%
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords from Contributors
Scientific Fields
Repository
A framework for machine learning in complex networks
Basic Info
- Host: GitHub
- Owner: TNanukem
- License: mit
- Language: Python
- Default Branch: main
- Size: 427 KB
Statistics
- Stars: 21
- Watchers: 1
- Forks: 5
- Open Issues: 0
- Releases: 2
Metadata Files
README.md
The sknet project is a scikit-learn and NetworkX compatible framework for machine learning in complex networks. It provides learning algorithms for complex networks, as well as transforming methods to turn tabular data into complex networks.
It started in 2021 as a project from volunteers to help to improve the development of research on the interface between complex networks and machine learning. It main focus is to help researchers and students to develop solutions using machine learning on complex networks.
:computer: Installation
The sknet installation is available via PiPy:
pip install scikit-net
:high_brightness: Quickstart
The following code snippet shows how one can transform tabular data into a complex network and then use it to create a classifier:
from sklearn.model_selection import train_test_split
from sklean.metrics import accuracy_score
from sklearn.datasets import load_iris
from sknet.network_construction import KNNConstructor
from sknet.supervised import EaseOfAccessClassifier
X, y = load_iris(return_X_y = True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
# The constructor responsible for transforming the tabular data into a complex network
knn_c = KNNConstructor(k=5)
classifier = EaseOfAccessClassifier()
classifier.fit(X_train, y_train, constructor=knn_c)
y_pred = classifier.predict(X_test)
accuracy_score(y_test, y_pred)
:pencil: Documentation
We provide an extensive API documentation as well with some user guides. The documentation is available on https://tnanukem.github.io/scikit-net/
Citation
If you used the scikit-net on your research project, please cite us using the following publication:
@article{Toledo2021,
doi = {10.21105/joss.03864},
url = {https://doi.org/10.21105/joss.03864},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {68},
pages = {3864},
author = {Tiago Toledo},
title = {sknet: A Python framework for Machine Learning in Complex Networks},
journal = {Journal of Open Source Software}
}
Owner
- Name: Tiago Toledo Junior
- Login: TNanukem
- Kind: user
- Location: São Carlos, SP, Brazil
- Repositories: 8
- Profile: https://github.com/TNanukem
I'm a Computer Engineer and Data Scientist. I'm interested in emulators, data science, cloud infrastructure and machine learning.
JOSS Publication
sknet: A Python framework for Machine Learning in Complex Networks
Authors
Instituto de Ciências Matemáticas e Computação, Universidade de São Paulo, Big Data
Editor
Øystein Sørensen
Tags
complex networks machine learning graph learning graphsGitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Tiago Toledo Junior | t****u@g****m | 95 |
| dependabot[bot] | 4****] | 1 |
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 10
- Total pull requests: 42
- Average time to close issues: 4 months
- Average time to close pull requests: 4 days
- Total issue authors: 5
- Total pull request authors: 3
- Average comments per issue: 1.2
- Average comments per pull request: 0.43
- Merged pull requests: 40
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- drj11 (5)
- imw (2)
- KONEONE (1)
- TNanukem (1)
- osorensen (1)
Pull Request Authors
- TNanukem (39)
- murilosoave (1)
- dependabot[bot] (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 29 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 4
- Total maintainers: 1
pypi.org: scikit-net
Machine Learning in Complex Networks
- Homepage: https://github.com/TNanukem/scikit-net
- Documentation: https://scikit-net.readthedocs.io/
- License: MIT
-
Latest release: 0.0.4
published over 2 years ago
Rankings
Maintainers (1)
Dependencies
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- iniconfig ==1.1.1
- joblib ==1.0.1
- networkx ==2.5
- numpy ==1.19.5
- packaging ==20.9
- pandas ==1.1.5
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- py ==1.10.0
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- pytest ==6.2.2
- python-dateutil ==2.8.1
- pytz ==2021.1
- scikit-learn ==0.24.1
- scipy ==1.5.4
- six ==1.15.0
- sklearn ==0.0
- threadpoolctl ==2.1.0
- toml ==0.10.2
- tqdm ==4.59.0
- typing-extensions ==3.7.4.3
- zipp ==3.4.1
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