easygraph.datasets.citation_graph module#

Cora, citeseer, pubmed dataset.

class easygraph.datasets.citation_graph.CitationGraphDataset(name, raw_dir=None, force_reload=False, verbose=True, reverse_edge=True, transform=None, reorder=False)[source]#

Bases: EasyGraphBuiltinDataset

The citation graph dataset, including Cora, CiteSeer and PubMed. Nodes mean authors and edges mean citation relationships.

Parameters:

name (str) – name can be ‘Cora’, ‘CiteSeer’ or ‘PubMed’.
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.
reverse_edge (bool) – Whether to add reverse edges in graph. Default: True.
transform (callable, optional) – A transform that takes in a Graph object and returns a transformed version. The Graph object will be transformed before every access.
reorder (bool) – Whether to reorder the graph using reorder_graph(). Default: False.

Attributes:

hash: Hash value for the dataset and the setting.
name: Name of the dataset.
num_classes
num_labels
raw_dir: Raw file directory contains the input data folder.
raw_path: Directory contains the input data files.
reverse_edge
save_dir: Directory to save the processed dataset.
save_name
save_path: Path to save the processed dataset.
url: Get url to download the raw dataset.
verbose: Whether to print information.

Methods

`download`()	Automatically download data and extract it.
`has_cache`()	Overwrite to realize your own logic of deciding whether there exists a cached dataset.
`load`()	Overwrite to realize your own logic of loading the saved dataset from files.
`process`()	Loads input data from data directory and reorder graph for better locality
`save`()	Overwrite to realize your own logic of saving the processed dataset into files.

has_cache()[source]#

Overwrite to realize your own logic of deciding whether there exists a cached dataset.

By default False.

property num_classes#

property num_labels#

process()[source]#

Loads input data from data directory and reorder graph for better locality

ind.name.x => the feature vectors of the training instances as scipy.sparse.csr.csr_matrix object; ind.name.tx => the feature vectors of the test instances as scipy.sparse.csr.csr_matrix object; ind.name.allx => the feature vectors of both labeled and unlabeled training instances

(a superset of ind.name.x) as scipy.sparse.csr.csr_matrix object;

ind.name.y => the one-hot labels of the labeled training instances as numpy.ndarray object; ind.name.ty => the one-hot labels of the test instances as numpy.ndarray object; ind.name.ally => the labels for instances in ind.name.allx as numpy.ndarray object; ind.name.graph => a dict in the format {index: [index_of_neighbor_nodes]} as collections.defaultdict

object;

ind.name.test.index => the indices of test instances in graph, for the inductive setting as list object.

property reverse_edge#

property save_name#

class easygraph.datasets.citation_graph.CiteseerGraphDataset(raw_dir=None, force_reload=False, verbose=True, reverse_edge=True, transform=None, reorder=False)[source]#

Bases: CitationGraphDataset

Citeseer citation network dataset.

Nodes mean scientific publications and edges mean citation relationships. Each node has a predefined feature with 3703 dimensions. The dataset is designed for the node classification task. The task is to predict the category of certain publication.

Statistics:

Nodes: 3327
Edges: 9228
Number of Classes: 6
Label Split:
- Train: 120
- Valid: 500
- Test: 1000

Parameters:

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.
reverse_edge (bool) – Whether to add reverse edges in graph. Default: True.
transform (callable, optional) – A transform that takes in a DGLGraph object and returns a transformed version. The DGLGraph object will be transformed before every access.
reorder (bool) – Whether to reorder the graph using reorder_graph(). Default: False.

num_classes#

Number of label classes

Type:: int

Notes

The node feature is row-normalized.

In citeseer dataset, there are some isolated nodes in the graph. These isolated nodes are added as zero-vecs into the right position.

Examples

>>> dataset = CiteseerGraphDataset()
>>> g = dataset[0]
>>> num_class = dataset.num_classes
>>>
>>> # get node feature
>>> feat = g.ndata['feat']
>>>
>>> # get data split
>>> train_mask = g.ndata['train_mask']
>>> val_mask = g.ndata['val_mask']
>>> test_mask = g.ndata['test_mask']
>>>
>>> # get labels
>>> label = g.ndata['label']

Attributes:

hash: Hash value for the dataset and the setting.
name: Name of the dataset.
num_classes
num_labels
raw_dir: Raw file directory contains the input data folder.
raw_path: Directory contains the input data files.
reverse_edge
save_dir: Directory to save the processed dataset.
save_name
save_path: Path to save the processed dataset.
url: Get url to download the raw dataset.
verbose: Whether to print information.

Methods

`download`()	Automatically download data and extract it.
`has_cache`()	Overwrite to realize your own logic of deciding whether there exists a cached dataset.
`load`()	Overwrite to realize your own logic of loading the saved dataset from files.
`process`()	Loads input data from data directory and reorder graph for better locality
`save`()	Overwrite to realize your own logic of saving the processed dataset into files.

class easygraph.datasets.citation_graph.CoraBinary(raw_dir=None, force_reload=False, verbose=True, transform=None)[source]#

Bases: EasyGraphBuiltinDataset

A mini-dataset for binary classification task using Cora.

After loaded, it has following members:

graphs : list of DGLGraph pmpds : list of scipy.sparse.coo_matrix labels : list of numpy.ndarray

Parameters:

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.
transform (callable, optional) – A transform that takes in a DGLGraph object and returns a transformed version. The DGLGraph object will be transformed before every access.

Attributes:

hash: Hash value for the dataset and the setting.
name: Name of the dataset.
raw_dir: Raw file directory contains the input data folder.
raw_path: Directory contains the input data files.
save_dir: Directory to save the processed dataset.
save_name
save_path: Path to save the processed dataset.
url: Get url to download the raw dataset.
verbose: Whether to print information.

Methods

`download`()	Automatically download data and extract it.
`has_cache`()	Overwrite to realize your own logic of deciding whether there exists a cached dataset.
`load`()	Overwrite to realize your own logic of loading the saved dataset from files.
`process`()	Overwrite to realize your own logic of processing the input data.
`save`()	Overwrite to realize your own logic of saving the processed dataset into files.

has_cache()[source]#

Overwrite to realize your own logic of deciding whether there exists a cached dataset.

By default False.

process()[source]#: Overwrite to realize your own logic of processing the input data.

property save_name#

class easygraph.datasets.citation_graph.CoraGraphDataset(raw_dir=None, force_reload=False, verbose=True, reverse_edge=True, transform=None, reorder=False)[source]#

Bases: CitationGraphDataset

Cora citation network dataset.

Nodes mean paper and edges mean citation relationships. Each node has a predefined feature with 1433 dimensions. The dataset is designed for the node classification task. The task is to predict the category of certain paper.

Statistics:

Nodes: 2708
Edges: 10556
Number of Classes: 7
Label split:
- Train: 140
- Valid: 500
- Test: 1000

Parameters:

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.
reverse_edge (bool) – Whether to add reverse edges in graph. Default: True.
transform (callable, optional) – A transform that takes in a DGLGraph object and returns a transformed version. The DGLGraph object will be transformed before every access.
reorder (bool) – Whether to reorder the graph using reorder_graph(). Default: False.

num_classes#

Number of label classes

Type:: int

Notes

The node feature is row-normalized.

Examples

>>> dataset = CoraGraphDataset()
>>> g = dataset[0]
>>> num_class = dataset.num_classes
>>>
>>> # get node feature
>>> feat = g.ndata['feat']
>>>
>>> # get data split
>>> train_mask = g.ndata['train_mask']
>>> val_mask = g.ndata['val_mask']
>>> test_mask = g.ndata['test_mask']
>>>
>>> # get labels
>>> label = g.ndata['label']

Attributes:

hash: Hash value for the dataset and the setting.
name: Name of the dataset.
num_classes
num_labels
raw_dir: Raw file directory contains the input data folder.
raw_path: Directory contains the input data files.
reverse_edge
save_dir: Directory to save the processed dataset.
save_name
save_path: Path to save the processed dataset.
url: Get url to download the raw dataset.
verbose: Whether to print information.

Methods

`download`()	Automatically download data and extract it.
`has_cache`()	Overwrite to realize your own logic of deciding whether there exists a cached dataset.
`load`()	Overwrite to realize your own logic of loading the saved dataset from files.
`process`()	Loads input data from data directory and reorder graph for better locality
`save`()	Overwrite to realize your own logic of saving the processed dataset into files.

class easygraph.datasets.citation_graph.PubmedGraphDataset(raw_dir=None, force_reload=False, verbose=True, reverse_edge=True, transform=None, reorder=False)[source]#

Bases: CitationGraphDataset

Pubmed citation network dataset.

Nodes mean scientific publications and edges mean citation relationships. Each node has a predefined feature with 500 dimensions. The dataset is designed for the node classification task. The task is to predict the category of certain publication.

Statistics:

Nodes: 19717
Edges: 88651
Number of Classes: 3
Label Split:
- Train: 60
- Valid: 500
- Test: 1000

Parameters:

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.
reverse_edge (bool) – Whether to add reverse edges in graph. Default: True.
transform (callable, optional) – A transform that takes in a DGLGraph object and returns a transformed version. The DGLGraph object will be transformed before every access.
reorder (bool) – Whether to reorder the graph using reorder_graph(). Default: False.

num_classes#

Number of label classes

Type:: int

Notes

The node feature is row-normalized.

Examples

>>> dataset = PubmedGraphDataset()
>>> g = dataset[0]
>>> num_class = dataset.num_of_class
>>>
>>> # get node feature
>>> feat = g.ndata['feat']
>>>
>>> # get data split
>>> train_mask = g.ndata['train_mask']
>>> val_mask = g.ndata['val_mask']
>>> test_mask = g.ndata['test_mask']
>>>
>>> # get labels
>>> label = g.ndata['label']

Attributes:

hash: Hash value for the dataset and the setting.
name: Name of the dataset.
num_classes
num_labels
raw_dir: Raw file directory contains the input data folder.
raw_path: Directory contains the input data files.
reverse_edge
save_dir: Directory to save the processed dataset.
save_name
save_path: Path to save the processed dataset.
url: Get url to download the raw dataset.
verbose: Whether to print information.

Methods

`download`()	Automatically download data and extract it.
`has_cache`()	Overwrite to realize your own logic of deciding whether there exists a cached dataset.
`load`()	Overwrite to realize your own logic of loading the saved dataset from files.
`process`()	Loads input data from data directory and reorder graph for better locality
`save`()	Overwrite to realize your own logic of saving the processed dataset into files.

easygraph.datasets.citation_graph.load_citeseer(raw_dir=None, force_reload=False, verbose=True, reverse_edge=True, transform=None)[source]#

Get CiteseerGraphDataset

Parameters:

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.
reverse_edge (bool) – Whether to add reverse edges in graph. Default: True.
transform (callable, optional) – A transform that takes in a DGLGraph object and returns a transformed version. The DGLGraph object will be transformed before every access.

Return type:

CiteseerGraphDataset

easygraph.datasets.citation_graph.load_cora(raw_dir=None, force_reload=False, verbose=True, reverse_edge=True, transform=None)[source]#

Get CoraGraphDataset

Parameters:

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.
reverse_edge (bool) – Whether to add reverse edges in graph. Default: True.
transform (callable, optional) – A transform that takes in a DGLGraph object and returns a transformed version. The DGLGraph object will be transformed before every access.

Return type:

CoraGraphDataset

easygraph.datasets.citation_graph.load_pubmed(raw_dir=None, force_reload=False, verbose=True, reverse_edge=True, transform=None)[source]#

Get PubmedGraphDataset

Parameters:

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.
reverse_edge (bool) – Whether to add reverse edges in graph. Default: True.
transform (callable, optional) – A transform that takes in a DGLGraph object and returns a transformed version. The DGLGraph object will be transformed before every access.

Return type:

PubmedGraphDataset