Louvain

SQL function: cugraph_louvain

Official cuGraph reference: C API

Build a hierarchy of communities by greedily moving vertices and aggregating partitions to maximize modularity.

Signature

cugraph_louvain(table_name [, src_col, dst_col [, weight_col [, options_json]]])

Relation inputs

The first positional argument names a registered edge table or view (the edges role). Parenthesized relation subqueries are not accepted; metadata validation uses the same registered name.

Vertex ID types

The edges relation declares the accepted vertex-ID domains. Numeric calls preserve the existing numeric schema. When logical string support is declared, Utf8, LargeUtf8, and Utf8View endpoint columns share one logical domain; their vertex-identity outputs are canonicalized to Utf8.

Domain	Accepted endpoint inputs	Output contract
Numeric edge endpoints	Int32, Int64	The numeric output schema is used for numeric calls.
Logical string edge endpoints	Utf8, LargeUtf8, Utf8View	Vertex identity columns are canonicalized to Utf8; scores, distances, counts, coordinates, and opaque labels remain numeric.

The native mapping type is Int64. Call-specific output schemas come from gpu_validate_call.

Logical string side-input limitations:

• edge ID columns and edge-ID predicate side inputs are not supported for logical string graphs

Scalar arguments & JSON options

Positional scalar arguments

src_col and dst_col name the edge endpoint columns; both are optional and default to src and dst.

Argument	Type	Required	Default	Notes
weight_col	Utf8|null	no		accepted as an edge-column binding; native algorithm execution does not consume weights; semantic effect: none for this algorithm

JSON options

Option	Type	Default	Constraints	Description
max_level	UInt32	100	min 1
resolution	Float64	1	> 0
threshold	Float64	1e-7	min 0

Graph construction options

This function builds an undirected graph by default (directed=false); all other graph construction options follow the shared defaults documented in Graph Construction Options.

Output schema

Column	Type	Nullable	Description
vertex	Int64|Utf8	no	Vertex assigned to a Louvain community.
partition	Int64	no	Community identifier assigned by Louvain.

These are generic descriptor schemas; validate the call to get the concrete, table-specific output schema.

Examples

This example runs on the citation network demo dataset.

Compare citation communities against field labels

Two views select the 2010s AI literature — nodes by field-of-study label, edges where both endpoints qualify — and Louvain partitions it by citation structure alone. Cross-tabulating each community against primary_fos (with a window function to keep the top 3 labels per community) shows how closely the detected communities align with the assigned labels:

CREATE VIEW ai_nodes AS
SELECT paper_id FROM papers
WHERE year >= 2010 AND primary_fos IN (
  'Deep learning', 'Artificial neural network', 'Convolutional neural network',
  'Recurrent neural network', 'Natural language processing',
  'Reinforcement learning', 'Image segmentation', 'Feature extraction',
  'Object detection', 'Speech recognition');

CREATE VIEW ai_edges AS
SELECT e.src, e.dst
FROM citation_edges e
JOIN ai_nodes a ON a.paper_id = e.src
JOIN ai_nodes b ON b.paper_id = e.dst;

WITH community_fos AS (
  SELECT c."partition" AS community, p.primary_fos, COUNT(*) AS n
  FROM cugraph_louvain('ai_edges', 'src', 'dst') c
  JOIN papers p ON p.paper_id = c.vertex
  GROUP BY c."partition", p.primary_fos),
ranked AS (
  SELECT SUM(n) OVER (PARTITION BY community) AS members,
         ROW_NUMBER() OVER (PARTITION BY community ORDER BY n DESC) AS rn,
         primary_fos,
         n
  FROM community_fos)
SELECT members, rn, primary_fos, n
FROM ranked
WHERE members > 2500 AND rn <= 3
ORDER BY members DESC, rn;

members	rn	primary_fos	n
10,684	1	Convolutional neural network	3,002
10,684	2	Object detection	2,467
10,684	3	Deep learning	2,247
6,707	1	Deep learning	2,168
6,707	2	Convolutional neural network	2,061
6,707	3	Feature extraction	778
5,118	1	Deep learning	1,591
5,118	2	Recurrent neural network	1,289
5,118	3	Convolutional neural network	714
3,960	1	Reinforcement learning	3,157
3,960	2	Artificial neural network	337
3,960	3	Deep learning	204

Louvain (1,960 communities over 38k papers) recovers recognizable subfield boundaries: a computer-vision community, a sequence-modeling community, and a reinforcement-learning community that is 80% one label. Note the quoted "partition", since the output column name is a SQL keyword. cugraph_leiden is a drop-in replacement with the same call shape.

Limitations & lifecycle

No algorithm-specific limitations.

Validate before running

Dry-run validation checks registered relation metadata, column presence, static dtypes, and options only; it does not scan edge data, construct a graph, or prove source-vertex existence:

SELECT * FROM gpu_validate_call(
  'cugraph_louvain',
  '{"schema_version":1,"relations":{"edges":{"table":"target_edges"}},"options":{"src_col":"src","dst_col":"dst"}}'
);

See GPU Function Catalog API for the full gpu_validate_call contract.