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| ```@meta | ||
| CurrentModule = GraphNeuralNetworks | ||
| ``` | ||
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| # Samplers | ||
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| ## Docs | ||
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| ```@autodocs | ||
| Modules = [GraphNeuralNetworks] | ||
| Pages = ["samplers.jl"] | ||
| Private = false | ||
| ``` |
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| # Datasets | ||
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| GraphNeuralNetworks.jl doesn't come with its own datasets, but leverages those available in the Julia (and non-Julia) ecosystem. In particular, the [examples in the GraphNeuralNetworks.jl repository](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/examples) make use of the [MLDatasets.jl](https://github.com/JuliaML/MLDatasets.jl) package. There you will find common graph datasets such as Cora, PubMed, Citeseer, TUDataset and [many others](https://juliaml.github.io/MLDatasets.jl/dev/datasets/graphs/). | ||
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| GraphNeuralNetworks.jl provides the [`mldataset2gnngraph`](@ref) method for interfacing with MLDatasets.jl. | ||
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| ```@docs | ||
| mldataset2gnngraph | ||
| ``` |
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| # GraphNeuralNetworks Monorepo | ||
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| This repository is a monorepo that contains all the code for the GraphNeuralNetworks project. The project is organized as a monorepo to facilitate code sharing and reusability across different components of the project. The monorepo contains the following packages: | ||
| This is the documentation page for [GraphNeuralNetworks.jl](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl), a graph neural network library written in Julia and based on the deep learning framework [Flux.jl](https://github.com/FluxML/Flux.jl). | ||
| GraphNeuralNetworks.jl is largely inspired by [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/), [Deep Graph Library](https://docs.dgl.ai/), | ||
| and [GeometricFlux.jl](https://fluxml.ai/GeometricFlux.jl/stable/). | ||
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| - `GraphNeuralNetwork.jl`: Package that contains stateful graph convolutional layers based on the machine learning framework [Flux.jl](https://fluxml.ai/Flux.jl/stable/). This is fronted package for Flux users. It depends on GNNlib.jl, GNNGraphs.jl, and Flux.jl packages. | ||
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| - `GNNLux.jl`: Package that contains stateless graph convolutional layers based on the machine learning framework [Lux.jl](https://lux.csail.mit.edu/stable/). This is fronted package for Lux users. It depends on GNNlib.jl, GNNGraphs.jl, and Lux.jl packages. | ||
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| - `GNNlib.jl`: Package that contains the core graph neural network layers and utilities. It depends on GNNGraphs.jl and GNNlib.jl packages and serves for code base for GraphNeuralNetwork.jl and GNNLux.jl packages. | ||
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| - `GNNGraphs.jl`: Package that contains the graph data structures and helper functions for working with graph data. It depends on Graphs.jl package. | ||
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| Here is a schema of the dependencies between the packages: | ||
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|  | ||
| * Implements common graph convolutional layers. | ||
| * Supports computations on batched graphs. | ||
| * Easy to define custom layers. | ||
| * CUDA support. | ||
| * Integration with [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl). | ||
| * [Examples](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/examples) of node, edge, and graph level machine learning tasks. | ||
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| Usage examples on real datasets can be found in the [examples](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/examples) folder. | ||
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| """ | ||
| 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 | ||
| ```jldoctest | ||
| 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)) | ||
| ``` | ||
| """ | ||
| struct NeighborLoader | ||
| graph::GNNGraph # The input GNNGraph (graph + features from GraphNeuralNetworks.jl) | ||
| num_neighbors::Vector{Int} # Number of neighbors to sample per node, for each layer | ||
| input_nodes::Vector{Int} # Set of input nodes (starting nodes for sampling) | ||
| num_layers::Int # Number of layers for neighborhood expansion | ||
| batch_size::Union{Int, Nothing} # Optional batch size, defaults to the length of input_nodes if not given | ||
| neighbors_cache::Dict{Int, Vector{Int}} # Cache neighbors to avoid recomputation | ||
| end | ||
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| function NeighborLoader(graph::GNNGraph; num_neighbors::Vector{Int}, input_nodes::Vector{Int}=nothing, | ||
| num_layers::Int, batch_size::Union{Int, Nothing}=nothing) | ||
| return NeighborLoader(graph, num_neighbors, input_nodes === nothing ? collect(1:graph.num_nodes) : input_nodes, num_layers, | ||
| batch_size === nothing ? length(input_nodes) : batch_size, Dict{Int, Vector{Int}}()) | ||
| end | ||
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| # Function to get cached neighbors or compute them | ||
| function get_neighbors(loader::NeighborLoader, node::Int) | ||
| if haskey(loader.neighbors_cache, node) | ||
| return loader.neighbors_cache[node] | ||
| else | ||
| neighbors = Graphs.neighbors(loader.graph, node, dir = :in) # Get neighbors from graph | ||
| loader.neighbors_cache[node] = neighbors | ||
| return neighbors | ||
| end | ||
| end | ||
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| # Function to sample neighbors for a given node at a specific layer | ||
| function sample_nbrs(loader::NeighborLoader, node::Int, layer::Int) | ||
| neighbors = get_neighbors(loader, node) | ||
| if isempty(neighbors) | ||
| return Int[] | ||
| else | ||
| num_samples = min(loader.num_neighbors[layer], length(neighbors)) # Limit to required samples for this layer | ||
| return rand(neighbors, num_samples) # Randomly sample neighbors | ||
| end | ||
| end | ||
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| # Iterator protocol for NeighborLoader with lazy batch loading | ||
| function Base.iterate(loader::NeighborLoader, state=1) | ||
| if state > length(loader.input_nodes) | ||
| return nothing # End of iteration if batches are exhausted (state larger than amount of input nodes or current batch no >= batch number) | ||
| end | ||
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| # Determine the size of the current batch | ||
| batch_size = min(loader.batch_size, length(loader.input_nodes) - state + 1) # Conditional in case there is not enough nodes to fill the last batch | ||
| batch_nodes = loader.input_nodes[state:state + batch_size - 1] # Each mini-batch uses different set of input nodes | ||
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| # Set for tracking the subgraph nodes | ||
| subgraph_nodes = Set(batch_nodes) | ||
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| for node in batch_nodes | ||
| # Initialize current layer of nodes (starting with the node itself) | ||
| sampled_neighbors = Set([node]) | ||
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| # For each GNN layer, sample the neighborhood | ||
| for layer in 1:loader.num_layers | ||
| new_neighbors = Set{Int}() | ||
| for n in sampled_neighbors | ||
| neighbors = sample_nbrs(loader, n, layer) # Sample neighbors of the node for this layer | ||
| new_neighbors = union(new_neighbors, neighbors) # Avoid duplicates in the neighbor set | ||
| end | ||
| sampled_neighbors = new_neighbors | ||
| subgraph_nodes = union(subgraph_nodes, sampled_neighbors) # Expand the subgraph with the new neighbors | ||
| end | ||
| end | ||
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| # Collect subgraph nodes and their features | ||
| subgraph_node_list = collect(subgraph_nodes) | ||
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| if isempty(subgraph_node_list) | ||
| return GNNGraph(), state + batch_size | ||
| end | ||
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| mini_batch_gnn = Graphs.induced_subgraph(loader.graph, subgraph_node_list) # Create a subgraph of the nodes | ||
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| # Continue iteration for the next batch | ||
| return mini_batch_gnn, state + batch_size | ||
| end |
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