Python Louvain Community Detection

Common errors

• ModuleNotFoundError: No module named 'python_louvain'

  cause The Python package `python-louvain` is installed, but the import statement uses the PyPI package name directly instead of its internal module name.
  fix The correct import path is `from community import community_louvain` or `import community`. [4, 11]

• AttributeError: module 'community' has no attribute 'best_partition'

  cause This typically occurs when a different package also named 'community' is installed, shadowing the `community` module provided by `python-louvain`. [11]
  fix Verify that `python-louvain` is correctly installed. If other 'community' packages exist, explicitly import `community_louvain` with `from community import community_louvain` or manage your virtual environment to avoid conflicts. You might need to uninstall conflicting packages or inspect your `sys.path`. [11]

• ERROR: Command errored out with exit status 1: ... Could not compile the C core of igraph.

  cause This error is common when attempting to install a *different* Louvain-related package (e.g., `louvain` which depends on `python-igraph` and its C core), not `python-louvain`. `python-louvain` relies on NetworkX and pure Python. [13, 21]
  fix Ensure you are installing `python-louvain` (which uses NetworkX) via `pip install python-louvain networkx`. If you intend to use the `louvain` package (which uses `python-igraph`), you will need to install `igraph`'s C core dependencies first, which varies by operating system. For `python-louvain`, this error is irrelevant. [13, 21]

Warnings

• gotcha The 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]
• gotcha The 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]
• gotcha The `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]

Install

• pip install python-louvain networkx Install with pip

Imports

• community_louvain
  wrong:

  import python_louvain

  correct:

  from community import community_louvain

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

  partition = community.best_partition(G)

  correct:

  partition = community_louvain.best_partition(G)

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

Quickstart

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)