# Louvain Clustering

Groups items using the Louvain clustering algorithm.

**Inputs**

- Data: input dataset

**Outputs**

- Data: dataset with cluster index as a class attribute
- Graph (with the Network addon): the weighted k-nearest neighbor graph

The widget first converts the input data into a k-nearest neighbor graph. To preserve the notions of distance, the Jaccard index for the number of shared neighbors is used to weight the edges. Finally, a modularity optimization community detection algorithm is applied to the graph to retrieve clusters of highly interconnected nodes. The widget outputs a new dataset in which the cluster index is used as a meta attribute.

- PCA processing is typically applied to the original data to remove noise.
- The distance metric is used for finding specified number of nearest neighbors.
- The number of nearest neighbors to use to form the KNN graph.
- Resolution is a parameter for the Louvain community detection algorithm that affects the size of the recovered clusters. Smaller resolutions recover smaller, and therefore a larger number of clusters, and conversely, larger values recover clusters containing more data points.
- When
*Apply Automatically*is ticked, the widget will automatically communicate all changes. Alternatively, click*Apply*.

## Example

*Louvain Clustering* converts the dataset into a graph, where it finds highly interconnected nodes. We can visualize the graph itself using the **Network Explorer** from the Network addon.

## References

Blondel, Vincent D., et al. “Fast unfolding of communities in large networks.” Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008.

Lambiotte, Renaud, J-C. Delvenne, and Mauricio Barahona. “Laplacian dynamics and multiscale modular structure in networks.” arXiv preprint, arXiv:0812.1770 (2008).