tau-community-detection 1.1.5

Community detection via Louvain/Leiden + Genetic Algorithm

TAU Community Detection

tau-community-detection implements TAU, an evolutionary community detection algorithm that couples genetic search with Leiden refinements. It is designed for scalable graph clustering with configurable hyper-parameters and multiprocessing support.

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Highlights

• Evolutionary search: Maintains a population of candidate partitions and applies crossover/mutation tailored for graph clustering.
• Leiden optimisation: Refines every candidate with Leiden to ensure modularity gains.
• Multiprocessing aware: Utilises worker pools for population optimisation.
• Deterministic options: Accepts a user-specified random seed for reproducibility.
• Simple API: Access everything through the TauClustering class.

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Installation

The project targets Python 3.10 or newer (required for slot-based dataclasses).

pip install tau-community-detection

To work from a clone, install the package in editable mode inside a virtual environment:

git clone https://github.com/HillelCharbit/community_TAU.git
cd community_TAU
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pip install -e .

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Quick Start (Python API)

from tau_community_detection import TauClustering
import networkx as nx

graph = nx.read_adjlist("path/to/graph.adjlist")

clustering = TauClustering(
    graph,
    population_size=80,
    max_generations=250,
)
membership, modularity_history = clustering.run()

print("community for node 0:", membership[0])
print("best modularity:", modularity_history[-1])

Need detailed per-generation metrics? Call run(track_stats=True) to receive (membership, modularity_history, generation_stats) where generation_stats is a list of dictionaries containing time and fitness diagnostics.

### Graph input

`TauClustering` accepts either an `igraph.Graph` or a `networkx.Graph` instance. Nodes are
internally remapped to contiguous integers to maximise igraph performance. If your data
resides on disk, load it into memory first (for example with `nx.read_adjlist`) before
initialising `TauClustering`.

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## Example Script

The repository ships with a runnable example that uses the bundled
`src/tau_community_detection/examples/example.graph` file. To execute it from the project
root:

```bash
python3 src/tau_community_detection/run_clustering.py

The script prints the detected membership vector and the modularity score history.

Note: multiprocessing may be restricted inside some sandboxed environments. Run the example on a local machine for best results.

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Configuration

All algorithm hyper-parameters live on the TauConfig dataclass. You can pass a custom configuration instance to TauClustering or adjust attributes on the default one. Key fields include:

• population_size: number of partitions maintained per generation.
• max_generations: upper bound on evolutionary iterations.
• elite_fraction / immigrant_fraction: govern selection pressure.
• stopping_generations / stopping_jaccard: convergence checks based on membership stability.
• random_seed: makes runs reproducible across processes.

See src/tau_community_detection/config.py for the complete list.

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Development

pip install -r requirements-dev.txt
make lint
make test

To build local distributions:

make build

Continuous Integration

• GitHub Actions run lint, tests, and package builds on pushes and pull requests.
• Set the CODECOV_TOKEN secret to upload coverage reports.

Publishing

1. Bump the version in setup.cfg/pyproject.toml and commit.
2. Tag the release with git tag vX.Y.Z && git push --tags.
3. Run the Publish Package workflow (defaults to TestPyPI). For PyPI, supply the pypi input and ensure PYPI_API_TOKEN is set. Use TEST_PYPI_API_TOKEN for dry runs.

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License

Released under the MIT License. See LICENSE for details.