grape.ensmallen.datasets.zenodo
This sub-module offers methods to automatically retrieve the graphs from Zenodo repository.
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"""This sub-module offers methods to automatically retrieve the graphs from Zenodo repository.""" from .gianttn import GiantTN __all__ = [ "GiantTN", ]
#  
def
GiantTN(
directed: bool = False,
preprocess: bool = True,
load_nodes: bool = True,
verbose: int = 2,
cache: bool = True,
cache_path: str = 'graphs/zenodo',
version: str = 'latest',
**additional_graph_kwargs: Dict
) -> grape.ensmallen.ensmallen.Graph:
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def GiantTN( directed: bool = False, preprocess: bool = True, load_nodes: bool = True, verbose: int = 2, cache: bool = True, cache_path: str = "graphs/zenodo", version: str = "latest", **additional_graph_kwargs: Dict ) -> Graph: """Return new instance of the GIANT-TN graph. The graph is automatically retrieved from the Zenodo repository. Parameters ------------------- directed: bool = False Wether to load the graph as directed or undirected. By default false. preprocess: bool = True Whether to preprocess the graph to be loaded in optimal time and memory. load_nodes: bool = True, Whether to load the nodes vocabulary or treat the nodes simply as a numeric range. verbose: int = 2, Wether to show loading bars during the retrieval and building of the graph. cache: bool = True Whether to use cache, i.e. download files only once and preprocess them only once. cache_path: str = "graphs" Where to store the downloaded graphs. version: str = "latest" The version of the graph to retrieve. additional_graph_kwargs: Dict Additional graph kwargs. Returns ----------------------- Instace of GIANT-TN graph. References --------------------- Please cite the following if you use the data: ```bib @article{yue2020graph, title={Graph embedding on biomedical networks: methods, applications and evaluations}, author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan}, journal={Bioinformatics}, volume={36}, number={4}, pages={1241--1251}, year={2020}, publisher={Oxford University Press} } ``` """ return AutomaticallyRetrievedGraph( graph_name="GiantTN", repository="zenodo", version=version, directed=directed, preprocess=preprocess, load_nodes=load_nodes, verbose=verbose, cache=cache, cache_path=cache_path, additional_graph_kwargs=additional_graph_kwargs )()
Return new instance of the GIANT-TN graph.
The graph is automatically retrieved from the Zenodo repository.
Parameters
- directed (bool = False): Wether to load the graph as directed or undirected. By default false.
- preprocess (bool = True): Whether to preprocess the graph to be loaded in optimal time and memory.
- load_nodes (bool = True,): Whether to load the nodes vocabulary or treat the nodes simply as a numeric range.
- verbose (int = 2,): Wether to show loading bars during the retrieval and building of the graph.
- cache (bool = True): Whether to use cache, i.e. download files only once and preprocess them only once.
- cache_path (str = "graphs"): Where to store the downloaded graphs.
- version (str = "latest"): The version of the graph to retrieve.
- additional_graph_kwargs (Dict): Additional graph kwargs.
Returns
- Instace of GIANT-TN graph.: References
Please cite the following if you use the data:
@article{yue2020graph,
title={Graph embedding on biomedical networks: methods, applications and evaluations},
author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
journal={Bioinformatics},
volume={36},
number={4},
pages={1241--1251},
year={2020},
publisher={Oxford University Press}
}