6.4 Implementing Custom Graph Samplers¶

Implementing custom samplers involves subclassing the dgl.dataloading.Sampler base class and implementing its abstract sample method. The sample method should take in two arguments:

def sample(self, g, indices):
    pass

The first argument g is the original graph to sample from while the second argument indices is the indices of the current mini-batch – it generally could be anything depending on what indices are given to the accompanied DataLoader but are typically seed node or seed edge IDs. The function returns the mini-batch of samples for the current iteration.

Note

The design here is similar to PyTorch’s torch.utils.data.DataLoader, which is an iterator of dataset. Users can customize how to batch samples using its collate_fn argument. Here in DGL, dgl.dataloading.DataLoader is an iterator of indices (e.g., training node IDs) while Sampler converts a batch of indices into a batch of graph- or tensor-type samples.

The code below implements a classical neighbor sampler:

class NeighborSampler(dgl.dataloading.Sampler):
    def __init__(self, fanouts : list[int]):
        super().__init__()
        self.fanouts = fanouts

    def sample(self, g, seed_nodes):
        output_nodes = seed_nodes
        subgs = []
        for fanout in reversed(self.fanouts):
            # Sample a fixed number of neighbors of the current seed nodes.
            sg = g.sample_neighbors(seed_nodes, fanout)
            # Convert this subgraph to a message flow graph.
            sg = dgl.to_block(sg, seed_nodes)
            seed_nodes = sg.srcdata[NID]
            subgs.insert(0, sg)
         input_nodes = seed_nodes
         return input_nodes, output_nodes, subgs

To use this sampler with DataLoader:

graph = ...  # the graph to be sampled from
train_nids = ...  # an 1-D tensor of training node IDs
sampler = NeighborSampler([10, 15])  # create a sampler
dataloader = dgl.dataloading.DataLoader(
    graph,
    train_nids,
    sampler,
    batch_size=32,    # batch_size decides how many IDs are passed to sampler at once
    ...               # other arguments
)
for i, mini_batch in enumerate(dataloader):
    # unpack the mini batch
    input_nodes, output_nodes, subgs = mini_batch
    train(input_nodes, output_nodes, subgs)

Sampler for Heterogeneous Graphs¶

To write a sampler for heterogeneous graphs, one needs to be aware that the argument g will be a heterogeneous graph while indices could be a dictionary of ID tensors. Most of DGL’s graph sampling operators (e.g., the sample_neighbors and to_block functions in the above example) can work on heterogeneous graph natively, so many samplers are automatically ready for heterogeneous graph. For example, the above NeighborSampler can be used on heterogeneous graphs:

hg = dgl.heterograph({
    ('user', 'like', 'movie') : ...,
    ('user', 'follow', 'user') : ...,
    ('movie', 'liked-by', 'user') : ...,
})
train_nids = {'user' : ..., 'movie' : ...}  # training IDs of 'user' and 'movie' nodes
sampler = NeighborSampler([10, 15])  # create a sampler
dataloader = dgl.dataloading.DataLoader(
    hg,
    train_nids,
    sampler,
    batch_size=32,    # batch_size decides how many IDs are passed to sampler at once
    ...               # other arguments
)
for i, mini_batch in enumerate(dataloader):
    # unpack the mini batch
    # input_nodes and output_nodes are dictionary while subgs are a list of
    # heterogeneous graphs
    input_nodes, output_nodes, subgs = mini_batch
    train(input_nodes, output_nodes, subgs)

Exclude Edges During Sampling¶

The examples above all belong to node-wise sampler because the indices argument to the sample method represents a batch of seed node IDs. Another common type of samplers is edge-wise sampler which, as name suggested, takes in a batch of seed edge IDs to construct mini-batch data. DGL provides a utility dgl.dataloading.as_edge_prediction_sampler() to turn a node-wise sampler to an edge-wise sampler. To prevent information leakge, it requires the node-wise sampler to have an additional third argument exclude_eids. The code below modifies the NeighborSampler we just defined to properly exclude edges from the sampled subgraph:

class NeighborSampler(Sampler):
    def __init__(self, fanouts):
        super().__init__()
        self.fanouts = fanouts

    # NOTE: There is an additional third argument. For homogeneous graphs,
    #   it is an 1-D tensor of integer IDs. For heterogeneous graphs, it
    #   is a dictionary of ID tensors. We usually set its default value to be None.
    def sample(self, g, seed_nodes, exclude_eids=None):
        output_nodes = seed_nodes
        subgs = []
        for fanout in reversed(self.fanouts):
            # Sample a fixed number of neighbors of the current seed nodes.
            sg = g.sample_neighbors(seed_nodes, fanout, exclude_edges=exclude_eids)
            # Convert this subgraph to a message flow graph.
            sg = dgl.to_block(sg, seed_nodes)
            seed_nodes = sg.srcdata[NID]
            subgs.insert(0, sg)
         input_nodes = seed_nodes
         return input_nodes, output_nodes, subgs

6.4 Implementing Custom Graph Samplers¶

Sampler for Heterogeneous Graphs¶

Exclude Edges During Sampling¶

Further Readings¶