Datasets:

ntgbaoo
/

Temporal_Graph_Scaling_TGS_Benchmark

Languages:

English

Size:

10B<n<100B

Tags:

croissant

Libraries:

License:

Dataset card Data Studio Files Files and versions

xet

Community

Dataset Preview

Split (1)

train

The full dataset viewer is not available (click to read why). Only showing a preview of the rows.

The dataset generation failed

Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      Integer value 100000000000000000 not in range: -9007199254740992 to 9007199254740992
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in cast_table_to_schema
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp>
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2116, in cast_array_to_feature
                  return array_cast(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
                  return func(array, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1963, in array_cast
                  return array.cast(pa_type)
                File "pyarrow/array.pxi", line 996, in pyarrow.lib.Array.cast
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/compute.py", line 404, in cast
                  return call_function("cast", [arr], options, memory_pool)
                File "pyarrow/_compute.pyx", line 590, in pyarrow._compute.call_function
                File "pyarrow/_compute.pyx", line 385, in pyarrow._compute.Function.call
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Integer value 100000000000000000 not in range: -9007199254740992 to 9007199254740992
              
              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/parquet_and_info.py", line 1529, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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blockNumber int64	timestamp int64	tokenAddress string	from string	to string	value float64	fileBlock int64
16,480,746	1,674,612,419	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x0000000000000000000000000000000000000000	0x0000ba9ff5c97f33bd62c216a56b3d02ae6ac4bb	1,000,000,000,000,000,000	26
16,480,778	1,674,612,803	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x0000ba9ff5c97f33bd62c216a56b3d02ae6ac4bb	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	1,000,000,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	750,000,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	250,000,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x707db4f918d0d4555632b59daa1c286e94766a16	11,249,900,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	3,749,970,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x710e7dbfe197d92a40b2929052befd39475aaf84	11,249,900,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	3,749,970,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x0b98dd25600bb9a521b1d54c03cf303028c0503d	11,249,900,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	3,749,970,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x62ec0fee10b49a2cbfc70c24dbb2bd5762dc597d	11,249,900,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	3,749,970,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x6394c899b65bafa2f38832aaaea2d1f8bdf53b88	11,249,900,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	3,749,970,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x69fa3e599057930b66d5a66bab7dbc1ed39a80e8	11,249,900,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	3,749,970,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0xf69fccb14f29f17bfc0dd53bb22d0e0afb95c773	11,249,900,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	3,749,970,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x63a41feb5ef98e0b5e31daf3ba3bec1a2d2ddee7	11,249,900,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	3,749,970,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x4659c378f1fae26ccc47690b9703c2dbd61ec282	11,249,900,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	3,749,970,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x15dca998f55cb149a418550e32034950c8729e71	11,249,900,000,000,000	26
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16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x2844d03c5b25a2cd3d05eb73871da63b24446eb8	3,266,160,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	1,088,720,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x11c6d083e9b0748be19bfce21115fe9f8ddd7fd9	2,635,520,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	878,506,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x11c6d083e9b0748be19bfce21115fe9f8ddd7fd9	2,604,560,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	868,186,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x71ad0b7557ad330002367f939118cdcaa48bfd2c	15,000,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	5,000,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0xb40bd8615a2b9292687b5735db242e7953deba01	14,925,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	4,975,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0xd179d7126e95a9ed359a315e2e3b93126392977d	14,175,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	4,725,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0xe7a6f9d44e175027212c3775854b66de38882336	14,175,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	4,725,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x4fab169c85876afa470a9f256f59514edcefcfe9	15,000,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	5,000,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0xb52a3094a6dc9a36c372c751ce596a84e46b95e7	14,925,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	4,975,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x0f11b24a2c1ec8aec6aea8b5db679cf02cde24c7	14,925,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	4,975,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0xaa75b925561e6944711b014617767b699c3c4966	14,925,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	4,975,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0xe95b3f559b9da49fbc3f95976a76d91168d854f8	14,925,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	4,975,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x4b2b31b7a32a3d3bb894effc31b16abd400bd258	8,196,760,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,732,250,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0xd103855b83ff26e0e6a67a0203e45455f37ca713	14,925,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	4,975,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x620a94c66fc27881ed03bcb6d7e4de95f6fc9b17	14,925,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	4,975,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x707db4f918d0d4555632b59daa1c286e94766a16	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	8,437,430,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x707db4f918d0d4555632b59daa1c286e94766a16	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,812,480,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x710e7dbfe197d92a40b2929052befd39475aaf84	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	8,437,430,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x710e7dbfe197d92a40b2929052befd39475aaf84	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,812,480,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x0b98dd25600bb9a521b1d54c03cf303028c0503d	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	8,437,430,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x0b98dd25600bb9a521b1d54c03cf303028c0503d	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,812,480,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x62ec0fee10b49a2cbfc70c24dbb2bd5762dc597d	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	8,437,430,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x62ec0fee10b49a2cbfc70c24dbb2bd5762dc597d	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,812,480,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x6394c899b65bafa2f38832aaaea2d1f8bdf53b88	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	8,437,430,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x6394c899b65bafa2f38832aaaea2d1f8bdf53b88	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,812,480,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x69fa3e599057930b66d5a66bab7dbc1ed39a80e8	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	8,437,430,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x69fa3e599057930b66d5a66bab7dbc1ed39a80e8	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,812,480,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0xf69fccb14f29f17bfc0dd53bb22d0e0afb95c773	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	8,437,430,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0xf69fccb14f29f17bfc0dd53bb22d0e0afb95c773	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,812,480,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x63a41feb5ef98e0b5e31daf3ba3bec1a2d2ddee7	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	8,437,430,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x63a41feb5ef98e0b5e31daf3ba3bec1a2d2ddee7	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,812,480,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x4659c378f1fae26ccc47690b9703c2dbd61ec282	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	8,437,430,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x4659c378f1fae26ccc47690b9703c2dbd61ec282	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,812,480,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x15dca998f55cb149a418550e32034950c8729e71	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	8,437,430,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x15dca998f55cb149a418550e32034950c8729e71	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,812,480,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x2844d03c5b25a2cd3d05eb73871da63b24446eb8	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	2,449,620,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x2844d03c5b25a2cd3d05eb73871da63b24446eb8	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	816,539,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0xdc1d18df7182b285a69502ab7c08d9a2aa2bdfae	14,925,000,000,000,000	26
16,480,788	1,674,612,923	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	4,975,000,000,000,000	26
16,480,789	1,674,612,935	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a803bad62e4024cce3be42e687596a9e8ae44f7	14,999,900,000,000,000	26
16,480,789	1,674,612,935	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	4,999,970,000,000,000	26
16,480,789	1,674,612,935	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x000000fc69ed6a4e257a349b04d75a8cf4b8a7c9	9,595,210,000,000,000	26
16,480,789	1,674,612,935	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	3,198,400,000,000,000	26
16,480,789	1,674,612,935	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x00000095ccc3d32c74b7873f521a8b461cb7139b	9,010,310,000,000,000	26
16,480,789	1,674,612,935	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	3,003,440,000,000,000	26
16,480,789	1,674,612,935	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x0000005331b74dca07c35a04892aab5e82a791dc	8,477,330,000,000,000	26
16,480,789	1,674,612,935	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,825,780,000,000,000	26
16,480,790	1,674,612,947	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x64d4363661ebe79d6227aa59eefcfe8a47dc92c5	1,703,320,000,000,000	26
16,480,790	1,674,612,947	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	567,775,000,000,000	26
16,480,790	1,674,612,947	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5cc9362c8ca705be9f0b6ae4834025c772e7839c	6,232,210,000,000,000	26
16,480,790	1,674,612,947	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,077,400,000,000,000	26
16,480,790	1,674,612,947	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x73f1285ec4bf64d50eed0a836be8d996c43d81b6	1,927,080,000,000,000	26
16,480,790	1,674,612,947	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	642,360,000,000,000	26
16,480,790	1,674,612,947	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0xec611c3b3be6908b2241fb3667a0794c3451f5bc	1,899,790,000,000,000	26
16,480,790	1,674,612,947	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	633,264,000,000,000	26
16,480,790	1,674,612,947	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x8a4da389e40682d4d886846c0723cb608317f523	1,873,080,000,000,000	26
16,480,790	1,674,612,947	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	624,359,000,000,000	26
16,480,790	1,674,612,947	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x38069d467678acb3fbb57bf909a24f8b52bbc112	2,456,860,000,000,000	26
16,480,790	1,674,612,947	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	818,953,000,000,000	26
16,480,791	1,674,612,959	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x581ecbd455f3b92725e346edb611a2e985c630ab	8,820,800,000,000,000	26
16,480,791	1,674,612,959	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	0x9ec9367b8c4dd45ec8e7b800b1f719251053ad60	0x5a3e6a77ba2f983ec0d371ea3b475f8bc0811ad5	2,940,270,000,000,000	26

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Towards Neural Scaling Laws for Foundation Models on Temporal Graphs

This repository provides the implementation of the TGS foundation model benchmarking and includes links to temporal networks suitable for foundation model training. TGS introduces a training process for foundation models using various real-world temporal networks, enabling prediction on previously unseen networks.

Overview

Temporal graph learning focuses on predicting future interactions from evolving network data. Our study addresses whether it's possible to predict the evolution of an unseen network within the same domain using observed temporal graphs. We introduce the Temporal Graph Scaling (TGS) dataset, comprising 84 ERC20 token transaction networks collected from 2017 to 2023. To evaluate transferability, we pre-train Temporal Graph Neural Networks (TGNNs) on up to 64 token transaction networks and assess their performance on 20 unseen token types. Our findings reveal that the neural scaling law observed in NLP and Computer Vision also applies to temporal graph learning: pre-training on more networks with more parameters enhances downstream performance. This is the first empirical demonstration of temporal graph transferability. Notably, the largest pre-trained model surpasses fine-tuned TGNNs on unseen test networks, marking a significant step towards building foundation models for temporal graphs. The code and datasets are publicly available.

TGS foundation model performance on unseen networks

Dataset

All extracted transaction networks required for foundation model training can be downloaded here.

The standard ML croissant repository for datasets TGS's metadata is also available here.

The TGS dataset extraction includes: (1) Token Extraction: extracting the token transaction network from our P2P Ethereum live node. (2) Discretizing: creating weekly snapshots for the Discretized Temporal Directed Graph (DTDG) setting. (3) Labeling: assigning labels based on network growth; increasing trends are labeled one, decreasing trends are labeled zero.

TGS dataset extraction

Benchmark Implementation

TGS transaction networks are divided randomly into train and test sets. The train set is used to train foundation models with different sizes; then, the trained models are evaluated on the test set.

TGS foundation model training overview

Prerequisites

Python 3.6+
Libraries listed in installed_packages.txt

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