docs: use Graph Data Council naming

README.md

@@ -44,8 +44,9 @@ DataFusion as extension operators, falling back to a join for any hop the CSR ca
   and `refresh()`es to new dataset versions.
 - **Smarter planning**: patterns start at their most selective node, and DataFusion gets column statistics to size
   its joins.
-- **Much wider Cypher**, enough to run the official LDBC Interactive complex queries (IC14 without `reduce()`,
-  which openCypher leaves out): `OPTIONAL MATCH`, `EXISTS` and pattern predicates, chained `WITH`, `UNWIND`,
+- **Much wider Cypher**. The Graph Data Council (GDC), formerly known as the Linked Data Benchmark Council (LDBC),
+  maintains the official Interactive complex queries. lance-csr runs them (IC14 without `reduce()`, which
+  openCypher leaves out), with `OPTIONAL MATCH`, `EXISTS` and pattern predicates, chained `WITH`, `UNWIND`,
   list and pattern comprehensions, maps, relationship alternation, `shortestPath`, `$parameters` and more,
   following openCypher's
   [standardisation scope](https://github.com/opencypher/openCypher/blob/master/docs/standardisation-scope.adoc).
@@ -160,16 +161,18 @@ r2 = engine.execute("SELECT name FROM person ORDER BY age DESC LIMIT 2")
 ## Benchmarks
 
 The benchmark runners and raw results live in two sibling repositories, next to the other engines' runners, so
-every number below can be reproduced from there. These are independent, unaudited measurements, not LDBC Benchmark
+every number below can be reproduced from there. These are independent, unaudited measurements, not GDC Benchmark
 Results. lance-csr runs with the CSR (`use_csr=True`) in two modes: *in memory* holds every table as Arrow in
 memory, and *lazy* reads Lance datasets from disk as queries need them. Neo4j runs as a server in a Docker VM; the
 other engines run embedded in the Python process. `docs/sync_benchmarks.py` regenerates the tables from the
 repositories' committed results.
 
-### LDBC SNB Interactive complex queries
+<a id="ldbc-snb-interactive-complex-queries"></a>
 
-[graph-benchmark-ldbc-complex](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc-complex) runs the
-14 complex read queries (IC1–IC14) of the LDBC SNB Interactive v1 workload over the SF1 dataset, with one fixed
+### GDC SNB Interactive complex queries
+
+[GDC Interactive benchmark repository](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc-complex) runs the
+14 complex read queries (IC1–IC14) of the GDC SNB Interactive v1 workload over the SF1 dataset, with one fixed
 parameter set per query and every result checked against a golden answer. lance-csr's
 [runner](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc-complex/tree/main/lance_csr) runs
 Neo4j's Cypher text for IC1–IC13 unchanged; IC14 replaces `reduce()`, which openCypher leaves out, with pattern
@@ -217,10 +220,12 @@ Mean query latency in milliseconds (lower is better, fastest per query in bold):
 
 <!-- /bench:ic:notes -->
 
-### LDBC SNB SF1, 30-query workload
+<a id="ldbc-snb-sf1-30-query-workload"></a>
+
+### GDC SNB SF1, 30-query workload
 
-[graph-benchmark-ldbc](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc) runs 30 custom Cypher
-queries over the LDBC SNB SF1 dataset (3.18M nodes, 17.3M relationships), with every result checked against an
+[GDC SF1 benchmark repository](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc) runs 30 custom Cypher
+queries over the GDC SNB SF1 dataset (3.18M nodes, 17.3M relationships), with every result checked against an
 expected answer. lance-csr's [runner](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc/tree/main/lance_csr)
 runs the same query text and Lance datasets as the lance-graph runner.
 

docs/benchmarks.md

@@ -16,18 +16,23 @@ process.
 The tables are generated from the repositories' committed results by `docs/sync_benchmarks.py`, which pins each
 repository to a commit.
 
-## LDBC SNB Interactive complex queries
+The Graph Data Council (GDC), formerly known as the Linked Data Benchmark Council (LDBC), maintains the
+Social Network Benchmark (SNB) used by both workloads below.
 
-[graph-benchmark-ldbc-complex](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc-complex) runs the
-14 complex read queries (IC1–IC14) of the LDBC Social Network Benchmark Interactive v1 workload over the SF1
+<a id="ldbc-snb-interactive-complex-queries"></a>
+
+## GDC SNB Interactive complex queries
+
+[GDC Interactive benchmark repository](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc-complex) runs the
+14 complex read queries (IC1–IC14) of the GDC Social Network Benchmark Interactive v1 workload over the SF1
 dataset, with one fixed parameter set per query and every result checked against a golden answer. lance-csr's
 [runner](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc-complex/tree/main/lance_csr) runs
 Neo4j's Cypher text for IC1–IC13 unchanged; IC14 replaces `reduce()`, which openCypher leaves out, with pattern
 comprehensions.
 
 !!! warning "Independent, unaudited measurements"
 
-    These are not official LDBC Benchmark Results. They were measured on one machine; your hardware, data and
+    These are not official GDC Benchmark Results. They were measured on one machine; your hardware, data and
     queries will differ. Use the benchmark repository to reproduce them.
 
 Over the 14 queries, lance-csr's speed relative to each engine:
@@ -72,17 +77,19 @@ Over the 14 queries, lance-csr's speed relative to each engine:
 
 <!-- /bench:ic:notes -->
 
-## LDBC SNB SF1, 30-query workload
+<a id="ldbc-snb-sf1-30-query-workload"></a>
+
+## GDC SNB SF1, 30-query workload
 
-[graph-benchmark-ldbc](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc) runs 30 custom Cypher
-queries over the LDBC Social Network Benchmark SF1 dataset (3.18M nodes, 17.3M relationships), with every result
+[GDC SF1 benchmark repository](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc) runs 30 custom Cypher
+queries over the GDC Social Network Benchmark SF1 dataset (3.18M nodes, 17.3M relationships), with every result
 checked against an expected answer. lance-csr's
 [runner](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc/tree/main/lance_csr) runs the same
 query text and Lance datasets as the lance-graph runner.
 
 !!! warning "Independent, unaudited measurements"
 
-    These are not official LDBC Benchmark Results. They were measured on one machine; your hardware, data and
+    These are not official GDC Benchmark Results. They were measured on one machine; your hardware, data and
     queries will differ. Use the benchmark repository to reproduce them.
 
 Over the 30 queries, lance-csr's speed relative to each engine:

docs/cypher.md

@@ -164,8 +164,9 @@ implemented; `ExecutionStrategy.LanceNative` is reserved and raises an error.
 
 ## Supported Cypher
 
-lance-csr implements a large read-only subset of openCypher, enough to run all 14 official
-[LDBC SNB Interactive](https://ldbcouncil.org/benchmarks/snb/) complex queries (IC14 without `reduce()`).
+The Graph Data Council (GDC), formerly known as the Linked Data Benchmark Council (LDBC), maintains the
+[GDC SNB Interactive](https://ldbcouncil.org/benchmarks/snb/) complex queries. lance-csr implements a large
+read-only subset of openCypher, enough to run all 14 (IC14 without `reduce()`).
 
 ### Clauses
 

docs/index.md

@@ -93,7 +93,8 @@ print(result.to_pylist())  # [{'friend': 'Bob'}, {'friend': 'Carol'}]
     around them.
 
 ### openCypher coverage
-:   Supports enough Cypher to run the official [LDBC](https://ldbcouncil.org/) Interactive complex queries:
+:   The Graph Data Council (GDC), formerly known as the Linked Data Benchmark Council (LDBC), maintains the
+    [GDC Interactive complex queries](https://ldbcouncil.org/). lance-csr supports enough Cypher to run them:
     `OPTIONAL MATCH`, `EXISTS`, chained `WITH`, `UNWIND`, list and pattern comprehensions,
     relationship alternation, variable-length paths, `shortestPath` and `$parameters`.
 

python/DEVELOPMENT.md

@@ -76,8 +76,9 @@ Rust Criterion benchmarks live in the `lance-graph-benches` crate:
 cargo bench -p lance-graph-benches --bench graph_execution -- --warm-up-time 1
 ```
 
-For comparisons with other graph engines on LDBC SNB SF1, see
-[graph-benchmark-ldbc](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc).
+The Graph Data Council (GDC), formerly known as the Linked Data Benchmark Council (LDBC), maintains the
+Social Network Benchmark (SNB). For comparisons with other graph engines on GDC SNB SF1, see
+[GDC SF1 benchmark repository](https://github.com/hyperdimensionalcomputingai/graph-benchmark-ldbc).
 
 ## Building wheels