Sigma Louvain Community Detection
100-node procedurally generated social network where the Louvain modularity algorithm (graphology-communities-louvain) is run client-side at render time to detect natural communities, then each community is assigned a distinct colour. Five planted communities of 20 nodes each are
Template details
- chart
- network
- graph
- sigma
- sigmajs
- webgl
- louvain
- community-detection
- social
When to use this template
This visualization helps identify inherent community structures within a network, making it easy to spot groups of nodes that are more densely connected to each other than to the rest of the network. Analysts use it to discover natural groupings in complex systems, understand modularity, and validate the effectiveness of community detection algorithms. It provides a clear visual signal for assessing the quality of detected communities.
- Network data with distinct nodes and weighted or unweighted edges.
- Graphs with a moderate number of nodes (hundreds to low thousands).
- Datasets where inherent community structure is hypothesized or sought.
- Data suitable for client-side graph processing and interactive layout.
- Networks where node attributes can enrich community interpretation.
- Systems requiring identification of densely connected subgroups.