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.

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']
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.

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']
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']
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