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class="content" id="documenter-page"><h1 id="Samplers"><a class="docs-heading-anchor" href="#Samplers">Samplers</a><a id="Samplers-1"></a><a class="docs-heading-anchor-permalink" href="#Samplers" title="Permalink"></a></h1><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" href="#GNNGraphs.NeighborLoader" id="GNNGraphs.NeighborLoader"><code>GNNGraphs.NeighborLoader</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">NeighborLoader(graph; num_neighbors, input_nodes, num_layers, [batch_size])</code></pre><p>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"}(https://arxiv.org/abs/1706.02216) paper.</p><p><strong>Fields</strong></p><ul><li><code>graph::GNNGraph</code>: The input graph.</li><li><code>num_neighbors::Vector{Int}</code>: A vector specifying the number of neighbors to sample per node at each GNN layer.</li><li><code>input_nodes::Vector{Int}</code>: A vector containing the starting nodes for neighbor sampling.</li><li><code>num_layers::Int</code>: The number of layers for neighborhood expansion (how far to sample neighbors).</li><li><code>batch_size::Union{Int, Nothing}</code>: The size of the batch. If not specified, it defaults to the number of <code>input_nodes</code>.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia hljs">julia> loader = NeighborLoader(graph; num_neighbors=[10, 5], input_nodes=[1, 2, 3], num_layers=2)
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