The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 84, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
SGPL local benchmark graphs
The 61-graph local corpus used for BFS, SSSP, connected components, and PageRank benchmark campaigns. This is the exact input corpus used in the local experiments through 24 October 2026; it is not the complete upstream graph collections.
Each graph is a separate .tar.gz archive. manifest.json records its original graph identifier, vertex/arc counts, direction settings, source vertex, synthetic-weight provenance, and SHA-256 checksums for the archive and every graph file. File bytes are preserved from the frozen benchmark inputs. No benchmark is run when packaging these files.
Files in each graph archive
original.edges: original benchmark edge-list input; integer vertex IDs, retaining its original occurrence order and duplicate edges.graph.bin: exact prepared binary CSR input used by the local graph benchmark harness. This is a benchmark-specific binary representation, not an Arrow or Parquet dataset. Its header is 64 bytes; vertex count and arc count are little-endian uint64 values at byte offsets 16 and 24. They are followed bynlittle-endian uint64 row offsets andmlittle-endian uint32 destination IDs. The terminal row offset is the header'sm.weighted-original.wel: original edge-list occurrences with the benchmark's synthetic SSSP weights added as a third column.weights.f32: little-endian float32 synthetic weights in prepared CSR arc order, used by the saved Graptor comparison.weights.i32: the same synthetic weights as little-endian signed int32 in prepared CSR arc order.
The original edge list and prepared CSR need not have the same row count. Use the manifest's direction/storage fields: undirected inputs are represented with both arc directions in CSR. Preserve duplicate edges and sparse vertex IDs when reproducing the benchmarks.
Synthetic SSSP weights
Weights are synthetic positive integers from 1 through 255, generated with seed 2358. They must not be described as original or real-world edge weights. The two weight sidecars have the same numeric values and differ in encoding. BFS, CC, and PageRank use the unweighted topology.
Download and extract one graph
from huggingface_hub import hf_hub_download
import tarfile
archive = hf_hub_download(repo_id="ksahib/graphs",
filename="dimacs__hamming8-2.tar.gz", repo_type="dataset")
with tarfile.open(archive, "r:gz") as graph_archive:
graph_archive.extractall("graphs", filter="data")
Integrity and provenance
SHA256SUMS covers the distributed archives and manifest. Each archive contains only the five graph-input files listed above. The package excludes source code, binaries, logs, credentials, private machine paths, and performance reports.
The graph identifiers and collection group names are retained from the frozen benchmark manifest. That manifest does not establish a corpus-wide redistribution license or complete original download URLs; no new license is asserted here. Consult the relevant upstream graph collection's terms before reuse.
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