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Tessera DB Tutorial: Detect Communities in Your Graph with Louvain
Find tightly connected clusters of entities — for fraud rings, customer segments, document topics, anything where 'who hangs out together' matters.
PAR2 Labs
August 29, 2026
3 min
Louvain is a modularity-optimising community detection algorithm. It finds clusters of nodes that are more densely connected to each other than to the rest of the graph — the algorithm of choice for most "who's coordinating with whom" questions.
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Before you start
Tier
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Community detection is part of Graph Data Science, an Enterprise feature.
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Step 1: Build the projection
Project your data into a graph for analysis. Because community membership is symmetric, treat the projection as undirected.
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Step 2: Run Louvain
Run Louvain over the projection with a resolution parameter (1.0 is a good starting point) and an iteration cap. Every node comes back tagged with a community id — same id means same community.
Every node comes back tagged with a community id — same id means same community.
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Step 3: Interpret the output
Group the nodes by community id and sort the groups by size to see the structure: a few large communities, a long tail of small ones. The largest and the tightest are usually the most interesting.
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Step 4: Tune the resolution parameter
The resolution parameter controls cluster granularity:
Below 1.0 — fewer, larger communities; good for broad themes.
Around 1.0 — the default; usually a sound starting point.
Above 1.0 — more, smaller communities; good for finding tight rings inside larger networks.
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Use case: fraud rings
Build the graph from transaction endpoints (sender, receiver) and run Louvain. Communities with high internal density and low external density are candidate fraud rings — accounts that transact with each other far more than with the broader population.
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Other algorithms in this family
Label propagation — faster, less precise.
Connected components — grouping by reachability rather than modularity.
Triangle count — local clustering density.
Key Takeaways
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Built an undirected projection, since community membership is symmetric.
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Run Louvain with a resolution parameter and an iteration cap, tagging every node with a community id.
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Grouped and sorted communities by size, and tuned the resolution for broader or tighter clusters.
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Applied the method to fraud rings, building the graph from sender and receiver endpoints.
PAR2 Labs · Technology
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