TemporalLayerNeighborSampler

class dgl.graphbolt.TemporalLayerNeighborSampler(datapipe, graph, fanouts, replace=False, prob_name=None, node_timestamp_attr_name=None, edge_timestamp_attr_name=None)[source]

Bases: TemporalNeighborSamplerImpl

Temporally sample neighbor edges from a graph and return sampled subgraphs.

Functional name: temporal_sample_layer_neighbor.

Sampler that builds computational dependency of node representations via labor sampling for multilayer GNN from the NeurIPS 2023 paper Layer-Neighbor Sampling – Defusing Neighborhood Explosion in GNNs

Layer-Neighbor sampler is responsible for sampling a subgraph from given data. It returns an induced subgraph along with compacted information. In the context of a node classification task, the neighbor sampler directly utilizes the nodes provided as seed nodes. However, in scenarios involving link prediction, the process needs another pre-process operation. That is, gathering unique nodes from the given node pairs, encompassing both positive and negative node pairs, and employs these nodes as the seed nodes for subsequent steps. When the graph is hetero, sampled subgraphs in minibatch will contain every edge type even though it is empty after sampling.

Implements the approach described in Appendix A.3 of the paper. Similar to dgl.dataloading.LaborSampler but this uses sequential poisson sampling instead of poisson sampling to keep the count of sampled edges per vertex deterministic like NeighborSampler. Thus, it is a drop-in replacement for NeighborSampler. However, unlike NeighborSampler, it samples fewer vertices and edges for multilayer GNN scenario without harming convergence speed with respect to training iterations.

Parameters:

  • datapipe (DataPipe) – The datapipe.

  • graph (FusedCSCSamplingGraph) – The graph on which to perform subgraph sampling.

  • fanouts (list[torch.Tensor] or list[int]) – The number of edges to be sampled for each node with or without considering edge types. The length of this parameter implicitly signifies the layer of sampling being conducted. Note: The fanout order is from the outermost layer to innermost layer. For example, the fanout β€˜[15, 10, 5]’ means that 15 to the outermost layer, 10 to the intermediate layer and 5 corresponds to the innermost layer.

  • replace (bool) – Boolean indicating whether the sample is preformed with or without replacement. If True, a value can be selected multiple times. Otherwise, each value can be selected only once.

  • prob_name (str, optional) – The name of an edge attribute used as the weights of sampling for each node. This attribute tensor should contain (unnormalized) probabilities corresponding to each neighboring edge of a node. It must be a 1D floating-point or boolean tensor, with the number of elements equalling the total number of edges.

  • node_timestamp_attr_name (str, optional) – The name of an node attribute used as the timestamps of nodes. It must be a 1D integer tensor, with the number of elements equalling the total number of nodes.

  • edge_timestamp_attr_name (str, optional) – The name of an edge attribute used as the timestamps of edges. It must be a 1D integer tensor, with the number of elements equalling the total number of edges.

Examples

TODO(zhenkun) : Add an example after the API to pass timestamps is finalized.