Samplers

NeighborLoader(graph; num_neighbors, input_nodes, num_layers, [batch_size])

A data structure for sampling neighbors from a graph for training Graph Neural Networks (GNNs). It supports multi-layer sampling of neighbors for a batch of input nodes, useful for mini-batch training originally introduced in "Inductive Representation Learning on Large Graphs" paper. [see https://arxiv.org/abs/1706.02216]

Fields

• graph::GNNGraph: The input graph.
• num_neighbors::Vector{Int}: A vector specifying the number of neighbors to sample per node at each GNN layer.
• input_nodes::Vector{Int}: A vector containing the starting nodes for neighbor sampling.
• num_layers::Int: The number of layers for neighborhood expansion (how far to sample neighbors).
• batch_size::Union{Int, Nothing}: The size of the batch. If not specified, it defaults to the number of input_nodes.

Usage

julia>  loader = NeighborLoader(graph; num_neighbors=[10, 5], input_nodes=[1, 2, 3], num_layers=2)

julia> batch_counter = 0
julia> for mini_batch_gnn in loader
            batch_counter += 1
            println("Batch ", batch_counter, ": Nodes in mini-batch graph: ", nv(mini_batch_gnn))