Registry / ai-ml / python-louvain

python-louvain

library0.16pypypiunverified

python-louvain is a Python implementation of the Louvain algorithm for community detection in large networks. It is built on top of the NetworkX framework. The current stable version is 0.16, released in January 2022. The project appears to be in a maintenance phase, with infrequent releases.

ai-ml data

pip install python-louvain networkx

github ↗package ↗

INSTALL

IMPORT

SIG · PYTHON-LOUVAIN

python-louvain

ai-mlpythonv0.16

Install

5.5s avg

Import

651ms

Disk

106MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.16 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

installs and imports cleanly · install 0.0s · import 0.655s · 105MB

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 5.5s · import 0.646s · 101MB

106MB installed

● package 106MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

community_louvain

✓ from community import community_louvain

✗ import python_louvain

The PyPI package is named 'python-louvain', but its main module is imported as 'community'. [4, 11]

best_partition

✓ partition = community_louvain.best_partition(G)

✗ partition = community.best_partition(G)

While 'import community' might work, explicitly importing 'community_louvain' is clearer and avoids potential conflicts with other 'community' packages. [11]

This quickstart demonstrates how to create a simple NetworkX graph and then apply the Louvain algorithm using `community_louvain.best_partition` to find community assignments. It also shows how to calculate the modularity of the resulting partition.

import networkx as nx from community import community_louvain # Create a sample graph G = nx.Graph() G.add_edges_from([(0, 1), (0, 2), (1, 2), (3, 4), (3, 5), (4, 5), (0, 3)]) # Compute the best partition using the Louvain method partition = community_louvain.best_partition(G) print("Graph Nodes:", G.nodes()) print("Detected Communities:", partition) # Example of how to get the modularity of the partition modularity = community_louvain.modularity(partition, G) print("Modularity:", modularity)

Debug

Known issues

gotchaThe package name for installation is `python-louvain`, but the primary module to import is `community`. This often leads to `ModuleNotFoundError` or confusion if users try `import python_louvain` or if another 'community' package is installed.

fix

Always use `from community import community_louvain` or `import community` after `pip install python-louvain`. Ensure no other packages named 'community' are conflicting in your environment. [4, 11, 16]

affects: All versions

gotchaThe Louvain algorithm is not strictly deterministic. Different runs on the same graph might yield slightly different partitions due to the order in which nodes are processed, especially in cases with multiple optimal solutions. [22]

fix

For reproducible results, use the `randomize=False` parameter in `best_partition` (if available in your version) or set a random seed for NumPy and Python's random module before graph creation or partition calculation, though direct control over the Louvain algorithm's internal randomness for reproducibility can be limited. [18]

affects: All versions

gotchaThe `python-louvain` package primarily supports undirected graphs. While NetworkX can handle directed graphs, applying `best_partition` to a directed graph often treats it as undirected. [24]

fix

If working with directed graphs where directionality is crucial for community detection, consider converting your graph to an undirected graph if appropriate for your analysis, or explore specialized algorithms for directed community detection (e.g., `DirectedLouvain` mentioned by users). [24]

affects: All versions

Upgrade

Version history

0.16latest on PyPI · released Jan 29, 2022

Audit

Dependencies

networkxrequiredpython-louvain is built on top of the NetworkX framework for graph representation and manipulation. While NetworkX 1.11 is mentioned in older docs, current versions are generally compatible. [1, 4]

Agent activity

17 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/taynaud/python-louvain ↗

packagehttps://pypi.org/project/python-louvain/ ↗