# dgl.rand_bipartite¶

dgl.rand_bipartite(utype, etype, vtype, num_src_nodes, num_dst_nodes, num_edges, idtype=torch.int64, device=device(type='cpu'))[source]

Generate a random uni-directional bipartite graph and return.

It uniformly chooses num_edges from all possible node pairs and form a graph. The random choice is without replacement, which means there will be no multi-edge in the resulting graph.

To control the randomness, set the random seed via dgl.seed().

Parameters
• utype (str, optional) – The name of the source node type.

• etype (str, optional) – The name of the edge type.

• vtype (str, optional) – The name of the destination node type.

• num_src_nodes (int) – The number of source nodes.

• num_dst_nodes (int) – The number of destination nodes.

• num_edges (int) – The number of edges

• idtype (int32, int64, optional) – The data type for storing the structure-related graph information such as node and edge IDs. It should be a framework-specific data type object (e.g., torch.int32). By default, DGL uses int64.

• device (Device context, optional) – The device of the resulting graph. It should be a framework-specific device object (e.g., torch.device). By default, DGL stores the graph on CPU.

Returns

The generated random bipartite graph.

Return type

DGLGraph

Examples

>>> import dgl
>>> dgl.rand_bipartite('user', 'buys', 'game', 50, 100, 10)
Graph(num_nodes={'game': 100, 'user': 50},