MiniGCDataset

class dgl.data.MiniGCDataset(num_graphs, min_num_v, max_num_v, seed=0, save_graph=True, force_reload=False, verbose=False, transform=None)[source]

Bases: DGLDataset

The synthetic graph classification dataset class.

The datset contains 8 different types of graphs.

class 0 : cycle graph
class 1 : star graph
class 2 : wheel graph
class 3 : lollipop graph
class 4 : hypercube graph
class 5 : grid graph
class 6 : clique graph
class 7 : circular ladder graph

Parameters:

num_graphs (int) – Number of graphs in this dataset.
min_num_v (int) – Minimum number of nodes for graphs
max_num_v (int) – Maximum number of nodes for graphs
seed (int, default is 0) – Random seed for data generation
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.

num_graphs

Number of graphs

Type:: int

min_num_v

The minimum number of nodes

Type:: int

max_num_v

The maximum number of nodes

Type:: int

num_classes

The number of classes

Type:: int

Examples

>>> data = MiniGCDataset(100, 16, 32, seed=0)

The dataset instance is an iterable

>>> len(data)
100
>>> g, label = data[64]
>>> g
Graph(num_nodes=20, num_edges=82,
      ndata_schemes={}
      edata_schemes={})
>>> label
tensor(5)

Batch the graphs and labels for mini-batch training

>>> graphs, labels = zip(*[data[i] for i in range(16)])
>>> batched_graphs = dgl.batch(graphs)
>>> batched_labels = torch.tensor(labels)
>>> batched_graphs
Graph(num_nodes=356, num_edges=1060,
      ndata_schemes={}
      edata_schemes={})

__getitem__(idx)[source]

Get the idx-th sample.

Parameters:: idx (int) – The sample index.
Returns:: The graph and its label.
Return type:: (dgl.Graph, Tensor)

__len__()[source]: Return the number of graphs in the dataset.