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The Graph Neural Network from the "Inductive Representation Learning on Large Graphs" paper, using the SAGEConv operator for message passing. The gaussian mixture model convolutional operator from the "Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs" paper. Conv1d module with lazy initialization of the in_channels argument of the Conv1d that is inferred from the input. The Adversarially Regularized Variational Graph Auto-Encoder model from the "Adversarially Regularized Graph Autoencoder for Graph Embedding" paper.

The Frequency Adaptive Graph Convolution operator from the "Beyond Low-Frequency Information in Graph Convolutional Networks" paper. The differentiable group normalization layer from the "Towards Deeper Graph Neural Networks with Differentiable Group Normalization" paper, which normalizes node features group-wise via a learnable soft cluster assignment. Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input x x x and target y y y of size ( N , C ) (N, C) ( N , C ). During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution.The Jumping Knowledge layer aggregation module from the "Representation Learning on Graphs with Jumping Knowledge Networks" paper. The continuous-filter convolutional neural network SchNet from the "SchNet: A Continuous-filter Convolutional Neural Network for Modeling Quantum Interactions" paper that uses the interactions blocks of the form.

The Graph Multiset Transformer pooling operator from the "Accurate Learning of Graph Representations with Graph Multiset Pooling" paper. The general, powerful, scalable (GPS) graph transformer layer from the "Recipe for a General, Powerful, Scalable Graph Transformer" paper. Applies message normalization over the aggregated messages as described in the "DeeperGCNs: All You Need to Train Deeper GCNs" paper.Memory based pooling layer from "Memory-Based Graph Networks" paper, which learns a coarsened graph representation based on soft cluster assignments. The path integral based pooling operator from the "Path Integral Based Convolution and Pooling for Graph Neural Networks" paper. Performs aggregations with one or more aggregators and combines aggregated results, as described in the "Principal Neighbourhood Aggregation for Graph Nets" and "Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions" papers.

InstanceNorm3d module with lazy initialization of the num_features argument of the InstanceNorm3d that is inferred from the input. The PointNet set layer from the "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" and "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space" papers. num_features , 64 ), 'x, edge_index -> x1' ), ReLU ( inplace = True ), ( GCNConv ( 64 , 64 ), 'x1, edge_index -> x2' ), ReLU ( inplace = True ), ( lambda x1 , x2 : [ x1 , x2 ], 'x1, x2 -> xs' ), ( JumpingKnowledge ( "cat" , 64 , num_layers = 2 ), 'xs -> x' ), ( global_mean_pool , 'x, batch -> x' ), Linear ( 2 * 64 , dataset .Applies layer normalization over each individual example in a batch of heterogeneous features as described in the "Layer Normalization" paper. Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . ConvTranspose1d module with lazy initialization of the in_channels argument of the ConvTranspose1d that is inferred from the input. The ARMA graph convolutional operator from the "Graph Neural Networks with Convolutional ARMA Filters" paper. The graph convolutional operator from the "Semi-supervised Classification with Graph Convolutional Networks" paper.