ECG

SQL function: cugraph_ecg

Official cuGraph reference: C API

Stabilize community assignments by combining an ensemble of randomized Louvain partitions into a final consensus clustering.

Quickstart

The call below supplies edges from registered relation target_edges with canonical src and dst columns and may include weight. Substitute your own registered relations.

SELECT * FROM cugraph_ecg(edges => (SELECT src, dst FROM target_edges));

Inputs

Every relation is a named parenthesized SELECT subquery. The required edges role uses canonical src and dst columns; every role, its canonical columns, and their accepted Arrow types are listed under Relation arguments. Metadata validation resolves registered tables named in its JSON request.

Endpoint columns accept numeric Int32, Int64 vertex IDs or logical string Utf8, LargeUtf8, Utf8View vertex IDs; string vertex-identity outputs are canonicalized to Utf8 (native mapping Int64) while scores, distances, counts, coordinates, and opaque labels stay numeric. The shared vertex-ID contract is summarized in Vertex ID support; the concrete call-specific schema comes 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

Arguments and options

Relation arguments

Argument	Required	Columns	Description
`edges`	yes	`src`, `dst`: `Int32`, `Int64`, `Utf8`, `LargeUtf8`, `Utf8View` `weight` (optional): `Float32`, `Float64` `edge_id` (optional): `Int32`, `Int64`	edge relation with canonical src and dst columns, plus optional weight and edge_id columns

Every edge_id column must have the integer type of src and dst; string-keyed graphs accept no edge IDs.

Named value arguments

Option	Type	Default	Constraints	Description
`ensemble_size`	`integer`	`10`	min 1; max 4294967295	number of ECG ensemble iterations
`max_level`	`integer`	`100`	min 1; max 4294967295	maximum hierarchy level, at least 1
`min_weight`	`number`	`0.001`	min 0	minimum ECG edge weight
`resolution`	`number`	`1`	> 0	positive community resolution
`seed`	`integer`	`0`	min 0; max 18446744073709552000	random initialization seed
`threshold`	`number`	`1e-7`	min 0	non-negative convergence threshold

Graph construction options

This function requires directed=false (undirected/symmetric graph); all other graph construction options follow the shared defaults documented in Graph Construction Options.

Output

Column	Type	Nullable	Description
`vertex`	`Int64\|Utf8`	no	Vertex assigned to an ECG community.
`partition`	`Int64`	no	Community identifier assigned by ECG.

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

Examples

This example runs on the citation network demo dataset.

Benchmark the ensemble against single-run algorithms

ECG runs an ensemble of Louvain passes (ensemble_size, default 10), reweights edges by how often their endpoints co-cluster, and clusters the consensus. Is that worth it? Because every cugraph_* function returns a plain relation, one statement can run all three community algorithms on the same subgraph (the Louvain example's 2010s AI views) and score them against the human-assigned primary_fos labels, representing the share of members in a community carrying its dominant label:

CREATE OR REPLACE 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 OR REPLACE 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 labeled AS (
  SELECT 'louvain' AS algorithm, c."partition" AS community, p.primary_fos
  FROM cugraph_louvain(edges => (SELECT src, dst FROM ai_edges)) c
  JOIN papers p ON p.paper_id = c.vertex
  UNION ALL
  SELECT 'leiden', c."partition", p.primary_fos
  FROM cugraph_leiden(edges => (SELECT src, dst FROM ai_edges)) c
  JOIN papers p ON p.paper_id = c.vertex
  UNION ALL
  SELECT 'ecg', c."partition", p.primary_fos
  FROM cugraph_ecg(edges => (SELECT src, dst FROM ai_edges)) c
  JOIN papers p ON p.paper_id = c.vertex),
counts AS (
  SELECT algorithm, community, primary_fos, COUNT(*) AS n
  FROM labeled GROUP BY 1, 2, 3),
sized AS (
  SELECT algorithm, community, SUM(n) AS members, MAX(n) AS top_label
  FROM counts GROUP BY 1, 2)
SELECT algorithm,
       COUNT(*) AS communities,
       COUNT(*) FILTER (WHERE members >= 100) AS ge100,
       ROUND(SUM(top_label) FILTER (WHERE members >= 100) * 100.0
             / SUM(members) FILTER (WHERE members >= 100), 1) AS purity_pct
FROM sized
GROUP BY algorithm
ORDER BY purity_pct DESC;

algorithm	communities	ge100	purity_pct
ecg	1,932	41	47.7
louvain	1,233	27	47.1
leiden	851	26	45.9

In this run the three GPU graph builds plus the joins and aggregation returned together in about one second on the capture host. ECG's consensus is deliberately conservative: it only keeps vertices together when most ensemble members agree, so on this subgraph it produced the finest partition (1,932 communities, 41 of them with 100+ papers) and scored above a single Louvain run on label purity. Determinism comes from seed (default 0); ensemble_size trades run time for consensus stability.

Limits

No algorithm-specific limitations.

To dry-run validate relation metadata, column types, and options without execution, see gpu_validate_call.

Quickstart​

Inputs​

Arguments and options​

Relation arguments​

Named value arguments​

Graph construction options​

Output​

Examples​

Benchmark the ensemble against single-run algorithms​

Limits​