DeepWalk¶

class dgl.nn.pytorch.DeepWalk(g, emb_dim=128, walk_length=40, window_size=5, neg_weight=1, negative_size=5, fast_neg=True, sparse=True)[source]¶

Bases: torch.nn.modules.module.Module

DeepWalk module from DeepWalk: Online Learning of Social Representations

For a graph, it learns the node representations from scratch by maximizing the similarity of node pairs that are nearby (positive node pairs) and minimizing the similarity of other random node pairs (negative node pairs).

Parameters

g (DGLGraph) – Graph for learning node embeddings
emb_dim (int, optional) – Size of each embedding vector. Default: 128
walk_length (int, optional) – Number of nodes in a random walk sequence. Default: 40
window_size (int, optional) – In a random walk w, a node w[j] is considered close to a node w[i] if i - window_size <= j <= i + window_size. Default: 5
neg_weight (float, optional) – Weight of the loss term for negative samples in the total loss. Default: 1.0
negative_size (int, optional) – Number of negative samples to use for each positive sample. Default: 5
fast_neg (bool, optional) – If True, it samples negative node pairs within a batch of random walks. Default: True
sparse (bool, optional) – If True, gradients with respect to the learnable weights will be sparse. Default: True

node_embed¶

Embedding table of the nodes

Type: nn.Embedding

Examples

>>> import torch
>>> from dgl.data import CoraGraphDataset
>>> from dgl.nn import DeepWalk
>>> from torch.optim import SparseAdam
>>> from torch.utils.data import DataLoader
>>> from sklearn.linear_model import LogisticRegression

>>> dataset = CoraGraphDataset()
>>> g = dataset[0]
>>> model = DeepWalk(g)
>>> dataloader = DataLoader(torch.arange(g.num_nodes()), batch_size=128,
...                         shuffle=True, collate_fn=model.sample)
>>> optimizer = SparseAdam(model.parameters(), lr=0.01)
>>> num_epochs = 5

>>> for epoch in range(num_epochs):
...     for batch_walk in dataloader:
...         loss = model(batch_walk)
...         optimizer.zero_grad()
...         loss.backward()
...         optimizer.step()

>>> train_mask = g.ndata['train_mask']
>>> test_mask = g.ndata['test_mask']
>>> X = model.node_embed.weight.detach()
>>> y = g.ndata['label']
>>> clf = LogisticRegression().fit(X[train_mask].numpy(), y[train_mask].numpy())
>>> clf.score(X[test_mask].numpy(), y[test_mask].numpy())

forward(batch_walk)[source]¶

Compute the loss for the batch of random walks

Parameters: batch_walk (torch.Tensor) – Random walks in the form of node ID sequences. The Tensor is of shape (batch_size, walk_length).
Returns: Loss value
Return type: torch.Tensor

reset_parameters()[source]¶: Reinitialize learnable parameters