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Error code: DatasetGenerationCastError Exception: DatasetGenerationCastError Message: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 6 new columns ({'label', 'i', 'u', 'Unnamed: 0', 'idx', 'ts'}) and 5 missing columns ({'timestamp', 'w', 'destination', 'state_label', 'source'}). This happened while the csv dataset builder was generating data using hf://datasets/WeiChow/DyGraphs_raw/CanParl/ml_CanParl.csv (at revision 961fa4edc995601219c1e5c6cb313691bcf37315) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations) Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, 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 2292, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast Unnamed: 0: int64 u: int64 i: int64 ts: double label: double idx: int64 -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 897 to {'source': Value(dtype='int64', id=None), 'destination': Value(dtype='int64', id=None), 'timestamp': Value(dtype='float64', id=None), 'state_label': Value(dtype='int64', id=None), 'w': Value(dtype='int64', id=None)} because column names don't match During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1417, 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 1049, in convert_to_parquet builder.download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare self._download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, 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 1741, 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 1872, in _prepare_split_single raise DatasetGenerationCastError.from_cast_error( datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 6 new columns ({'label', 'i', 'u', 'Unnamed: 0', 'idx', 'ts'}) and 5 missing columns ({'timestamp', 'w', 'destination', 'state_label', 'source'}). This happened while the csv dataset builder was generating data using hf://datasets/WeiChow/DyGraphs_raw/CanParl/ml_CanParl.csv (at revision 961fa4edc995601219c1e5c6cb313691bcf37315) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
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source
int64 | destination
int64 | timestamp
float64 | state_label
int64 | w
int64 |
---|---|---|---|---|
383 | 352 | 0 | 0 | 6 |
410 | 352 | 0 | 0 | 6 |
675 | 352 | 0 | 0 | 6 |
466 | 352 | 0 | 0 | 6 |
119 | 352 | 0 | 0 | 6 |
148 | 352 | 0 | 0 | 2 |
203 | 352 | 0 | 0 | 6 |
52 | 352 | 0 | 0 | 2 |
341 | 352 | 0 | 0 | 2 |
79 | 352 | 0 | 0 | 6 |
522 | 352 | 0 | 0 | 6 |
411 | 352 | 0 | 0 | 2 |
157 | 352 | 0 | 0 | 6 |
535 | 352 | 0 | 0 | 6 |
710 | 352 | 0 | 0 | 6 |
711 | 352 | 0 | 0 | 6 |
5 | 352 | 0 | 0 | 6 |
80 | 352 | 0 | 0 | 2 |
456 | 352 | 0 | 0 | 6 |
718 | 352 | 0 | 0 | 6 |
380 | 352 | 0 | 0 | 6 |
504 | 352 | 0 | 0 | 6 |
355 | 352 | 0 | 0 | 6 |
217 | 352 | 0 | 0 | 6 |
642 | 352 | 0 | 0 | 6 |
216 | 352 | 0 | 0 | 6 |
609 | 352 | 0 | 0 | 6 |
704 | 352 | 0 | 0 | 6 |
409 | 352 | 0 | 0 | 6 |
57 | 352 | 0 | 0 | 6 |
565 | 352 | 0 | 0 | 2 |
707 | 352 | 0 | 0 | 6 |
102 | 352 | 0 | 0 | 6 |
455 | 352 | 0 | 0 | 6 |
230 | 352 | 0 | 0 | 2 |
467 | 352 | 0 | 0 | 6 |
281 | 352 | 0 | 0 | 6 |
733 | 352 | 0 | 0 | 6 |
526 | 352 | 0 | 0 | 6 |
362 | 352 | 0 | 0 | 6 |
602 | 352 | 0 | 0 | 6 |
173 | 352 | 0 | 0 | 6 |
468 | 352 | 0 | 0 | 6 |
691 | 352 | 0 | 0 | 6 |
365 | 352 | 0 | 0 | 2 |
400 | 352 | 0 | 0 | 6 |
661 | 352 | 0 | 0 | 6 |
538 | 352 | 0 | 0 | 6 |
517 | 352 | 0 | 0 | 6 |
14 | 352 | 0 | 0 | 6 |
649 | 352 | 0 | 0 | 6 |
681 | 352 | 0 | 0 | 6 |
138 | 352 | 0 | 0 | 6 |
591 | 352 | 0 | 0 | 6 |
182 | 352 | 0 | 0 | 6 |
342 | 352 | 0 | 0 | 6 |
65 | 352 | 0 | 0 | 6 |
135 | 352 | 0 | 0 | 6 |
379 | 352 | 0 | 0 | 6 |
166 | 352 | 0 | 0 | 6 |
194 | 352 | 0 | 0 | 6 |
596 | 352 | 0 | 0 | 6 |
391 | 352 | 0 | 0 | 2 |
397 | 352 | 0 | 0 | 6 |
427 | 352 | 0 | 0 | 6 |
450 | 352 | 0 | 0 | 6 |
110 | 352 | 0 | 0 | 6 |
629 | 352 | 0 | 0 | 6 |
113 | 352 | 0 | 0 | 6 |
241 | 352 | 0 | 0 | 6 |
344 | 352 | 0 | 0 | 6 |
412 | 352 | 0 | 0 | 6 |
401 | 352 | 0 | 0 | 2 |
592 | 352 | 0 | 0 | 6 |
512 | 352 | 0 | 0 | 6 |
496 | 352 | 0 | 0 | 6 |
701 | 352 | 0 | 0 | 6 |
7 | 352 | 0 | 0 | 6 |
671 | 352 | 0 | 0 | 6 |
197 | 352 | 0 | 0 | 6 |
333 | 352 | 0 | 0 | 6 |
662 | 352 | 0 | 0 | 6 |
562 | 352 | 0 | 0 | 6 |
600 | 352 | 0 | 0 | 6 |
253 | 352 | 0 | 0 | 9 |
713 | 352 | 0 | 0 | 9 |
595 | 352 | 0 | 0 | 9 |
343 | 352 | 0 | 0 | 9 |
571 | 352 | 0 | 0 | 9 |
674 | 352 | 0 | 0 | 9 |
111 | 352 | 0 | 0 | 9 |
142 | 352 | 0 | 0 | 9 |
184 | 352 | 0 | 0 | 9 |
360 | 352 | 0 | 0 | 9 |
64 | 352 | 0 | 0 | 9 |
572 | 352 | 0 | 0 | 9 |
331 | 352 | 0 | 0 | 9 |
196 | 352 | 0 | 0 | 9 |
325 | 352 | 0 | 0 | 9 |
31 | 352 | 0 | 0 | 9 |
license: apache-2.0 tags: - text - graph task_categories: - graph-ml language: - en datasets: format: csv
The dataset is dynamic graphs for paper CrossLink. The usage of this dataset can be seen in Github
π Introduction
CrossLink learns the evolution pattern of a specific downstream graph and subsequently makes pattern-specific link predictions. It employs a technique called conditioned link generation, which integrates both evolution and structure modeling to perform evolution-specific link prediction. This conditioned link generation is carried out by a transformer-decoder architecture, enabling efficient parallel training and inference. CrossLink is trained on extensive dynamic graphs across diverse domains, encompassing 6 million dynamic edges. Extensive experiments on eight untrained graphs demonstrate that CrossLink achieves state-of-the-art performance in cross-domain link prediction. Compared to advanced baselines under the same settings, CrossLink shows an average improvement of 11.40% in Average Precision across eight graphs. Impressively, it surpasses the fully supervised performance of 8 advanced baselines on 6 untrained graphs.
Format
Please keep the dataset in the fellow format:
Unnamed: 0 | u | i | ts | label | idx |
---|---|---|---|---|---|
idx-1 |
source node |
target node |
interaction time |
defalut: 0 |
from 1 to the #edges |
You can prepare those data by the code in preprocess_data
folder
You can also use our processed data in huggingface
π Citation
If you find this work helpful, please consider citing:
@misc{huang2024graphmodelcrossdomaindynamic,
title={One Graph Model for Cross-domain Dynamic Link Prediction},
author={Xuanwen Huang and Wei Chow and Yang Wang and Ziwei Chai and Chunping Wang and Lei Chen and Yang Yang},
year={2024},
eprint={2402.02168},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2402.02168},
}
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