SBMMixtureDataset

class dgl.data.SBMMixtureDataset(n_graphs, n_nodes, n_communities, k=2, avg_deg=3, pq='Appendix_C', rng=None)

Bases: DGLDataset

Symmetric Stochastic Block Model Mixture

Reference: Appendix C of Supervised Community Detection with Hierarchical Graph Neural Networks

Parameters:

• n_graphs (int) – Number of graphs.

• n_nodes (int) – Number of nodes.

• n_communities (int) – Number of communities.

• k (int, optional) – Multiplier. Default: 2

• avg_deg (int, optional) – Average degree. Default: 3

• pq (list of pair of nonnegative float or str, optional) – Random densities. This parameter is for future extension, for now it’s always using the default value. Default: Appendix_C

• rng (numpy.random.RandomState, optional) – Random number generator. If not given, it’s numpy.random.RandomState() with seed=None, which read data from /dev/urandom (or the Windows analogue) if available or seed from the clock otherwise. Default: None

Raises:

RuntimeError is raised if pq is not a list or string. –

Examples

>>> data = SBMMixtureDataset(n_graphs=16, n_nodes=10000, n_communities=2)
>>> from torch.utils.data import DataLoader
>>> dataloader = DataLoader(data, batch_size=1, collate_fn=data.collate_fn)
>>> for graph, line_graph, graph_degrees, line_graph_degrees, pm_pd in dataloader:
...     # your code here

__getitem__(idx)

Get one example by index

Parameters:

idx (int) – Item index

Returns:

• graph (dgl.DGLGraph) – The original graph

• line_graph (dgl.DGLGraph) – The line graph of graph

• graph_degree (numpy.ndarray) – In degrees for each node in graph

• line_graph_degree (numpy.ndarray) – In degrees for each node in line_graph

• pm_pd (numpy.ndarray) – Edge indicator matrices Pm and Pd

__len__()

Number of graphs in the dataset.

collate_fn(x)

The collate function for dataloader

Parameters:

x (tuple) –

a batch of data that contains:

• graph: dgl.DGLGraph

  The original graph

• line_graph: dgl.DGLGraph

  The line graph of graph

• graph_degree: numpy.ndarray

  In degrees for each node in graph

• line_graph_degree: numpy.ndarray

  In degrees for each node in line_graph

• pm_pd: numpy.ndarray

  Edge indicator matrices Pm and Pd

Returns:

• g_batch (dgl.DGLGraph) – Batched graphs

• lg_batch (dgl.DGLGraph) – Batched line graphs

• degg_batch (numpy.ndarray) – A batch of in degrees for each node in g_batch

• deglg_batch (numpy.ndarray) – A batch of in degrees for each node in lg_batch

• pm_pd_batch (numpy.ndarray) – A batch of edge indicator matrices Pm and Pd