![]() The simple spectral graph convolutional operator from the "Simple Spectral Graph Convolution" paper The simple graph convolutional operator from the "Simplifying Graph Convolutional Networks" paper The ARMA graph convolutional operator from the "Graph Neural Networks with Convolutional ARMA Filters" paper The modified GINConv operator from the "Strategies for Pre-training Graph Neural Networks" paper The graph isomorphism operator from the "How Powerful are Graph Neural Networks?" paper The topology adaptive graph convolutional networks operator from the "Topology Adaptive Graph Convolutional Networks" paper The graph attentional propagation layer from the "Attention-based Graph Neural Network for Semi-Supervised Learning" paper ![]() The graph transformer operator from the "Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification" paper The GATv2 operator from the "How Attentive are Graph Attention Networks?" paper, which fixes the static attention problem of the standard GATConv layer. The fused graph attention operator from the "Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective" paper. The graph attentional operator from the "Graph Attention Networks" paper. The graph attentional operator from the "Graph Attention Networks" paper The residual gated graph convolutional operator from the "Residual Gated Graph ConvNets" paper The gated graph convolution operator from the "Gated Graph Sequence Neural Networks" paper The GravNet operator from the "Learning Representations of Irregular Particle-detector Geometry with Distance-weighted Graph Networks" paper, where the graph is dynamically constructed using nearest neighbors. The graph neural network operator from the "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks" paper The GraphSAGE operator from the "Inductive Representation Learning on Large Graphs" paper. The GraphSAGE operator from the "Inductive Representation Learning on Large Graphs" paper The chebyshev spectral graph convolutional operator from the "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering" paper The graph convolutional operator from the "Semi-supervised Classification with Graph Convolutional Networks" paper Convolutional Layers īase class for creating message passing layers of the formĪ simple message passing operator that performs (non-trainable) propagation X_dict ( Dict ) – A dictionary holding inputįeatures for each individual type. ![]() forward ( x_dict : Dict ) → Dict Parameters Resets all learnable parameters of the module. **kwargs ( optional) – Additional arguments of Types ( List, optional) – The keys of the input dictionary. Out_channels ( int) – Size of each output sample. Initialized lazily in case it is given as -1. Passed an integer, types will be a mandatory argument. In_channels ( int or Dict ) – Size of each input sample. It supports lazy initialization and customizable weight and bias OrderedDict of modules (and function header definitions) canĬlass Linear ( in_channels : int, out_channels : int, bias : bool = True, weight_initializer : Optional = None, bias_initializer : Optional = None ) Īpplies a linear tranformation to the incoming data Input_args ( str) – The input arguments of the model. ![]() From torch.nn import Linear, ReLU, Dropout from torch_geometric.nn import Sequential, GCNConv, JumpingKnowledge from torch_geometric.nn import global_mean_pool model = Sequential ( 'x, edge_index, batch', , 'x1, x2 -> xs' ), ( JumpingKnowledge ( "cat", 64, num_layers = 2 ), 'xs -> x' ), ( global_mean_pool, 'x, batch -> x' ), Linear ( 2 * 64, dataset. ![]()
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