Package for 'Towards better dynamic link prediction' paper at NeurIPS 2022, the code provides download, processing, dataloading and evaluation code for a suite of dynamic graph datasets.
Project description
DGB
Provides
- provides a data-loader to download a suite of dynamic graph datasets.
- provides an evaluator for the suit of dynamic graph datasets
- will provide tools to benchmark graph models example code snippets
To install
use pip for Python, make sure version python version is 3.6+
>>> pip install DGB
Code examples
Code snippet to import the module::
import DGB
Code snippets to download a dataset::
enron = DGB.TemporalDataSets(data_name="enron")
enron_dict = enron.process()
train = enron_dict["train"]
test = enron_dict["test"]
val = enron_dict["validation"]
to print all possible datasets::
data_list = DGB.list_all()
for data_name in data_list:
print(data_name)
to download all possible datasets that have not been downloaded yet::
DGB.download_all()
to force redownload all datasets::
DGB.force_download_all()
to skip download prompts and dataset statistics when processing::
DGB.TemporalDataSets(data_name="enron", skip_download_prompt=True, data_set_statistics=False)
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