Scikit-network

0.33.5 · active · verified Mon Apr 13

Scikit-network is a Python library for the analysis of large graphs, providing state-of-the-art algorithms for clustering, classification, ranking, embedding, and more. It is currently at version 0.33.5 and actively maintained with frequent minor releases that include new features, bug fixes, and Python version support.

Warnings

breaking Major API changes for clustering, classification, ranking, and GNN algorithms. Methods like `fit_transform` were introduced, and parameter names were changed (e.g., `seeds` to `labels` for classification, `seeds` to `weights` for ranking).
Fix: Review the documentation for `sknetwork.clustering`, `sknetwork.classification`, `sknetwork.ranking`, and `sknetwork.gnn` modules. Update method calls and parameter names to match the new API (e.g., use `fit_predict` or `fit_transform`).
breaking The parameter `membership` was renamed to `probs` in soft classification and clustering algorithms.
Fix: If you were using the `membership` parameter in soft classification or clustering algorithms, update your code to use `probs` instead.
breaking Support for older Python versions has been dropped. Python 3.9 was dropped in 0.33.5, and Python 3.8 was dropped in 0.33.0.
Fix: Ensure your Python environment is running version 3.10 or newer. Upgrade your Python interpreter if necessary to use the latest scikit-network versions.
deprecated The 'hierarchical Louvain embedding' feature was removed.
Fix: If your application relied on hierarchical Louvain embedding, you will need to find an alternative approach or algorithm for hierarchical graph analysis, as this specific feature is no longer available.
deprecated First-order methods for link prediction were removed.
Fix: Migrate to newer link prediction methods provided by the library, such as nearest neighbor methods, which were introduced alongside this change. Consult the `sknetwork.link_prediction` module documentation.

Install

pip install scikit-network Install stable version

Imports

Louvain

from sknetwork.clustering import Louvain

Common clustering algorithm.

KarateClub
```
from sknetwork.data import karate_club
```
Example graph dataset for quick experimentation.
DiffusionClassifier
```
from sknetwork.classification import DiffusionClassifier
```
Example of a classification algorithm.
PageRank
```
from sknetwork.ranking import PageRank
```
Example of a ranking algorithm.

Quickstart

This quickstart demonstrates how to load a sample graph and apply the Louvain clustering algorithm to detect communities. It prints the number of nodes, a snippet of the assigned labels, and the total number of unique clusters found.

from sknetwork.data import karate_club
from sknetwork.clustering import Louvain

# Load an example graph (Karate Club dataset)
graph = karate_club()
adjacency = graph.adjacency

# Apply Louvain clustering algorithm
louvain = Louvain()
labels = louvain.fit_predict(adjacency)

print(f"Number of nodes: {adjacency.shape[0]}")
print(f"Detected cluster labels (first 10): {labels[:10]}")
print(f"Unique clusters found: {len(set(labels))}")

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