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The Graph Neural Network from the "Semi-supervised Classification with Graph Convolutional Networks" paper, using the GCNConv operator for message passing. The pathfinder discovery network convolutional operator from the "Pathfinder Discovery Networks for Neural Message Passing" paper.

The Heterogeneous Graph Transformer (HGT) operator from the "Heterogeneous Graph Transformer" paper. A generic wrapper for computing graph convolution on directed graphs as described in the "Edge Directionality Improves Learning on Heterophilic Graphs" paper. Applies batch normalization over a batch of heterogeneous features as described in the "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" paper. The Efficient Graph Convolution from the "Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions" paper. Creates a criterion that optimizes a two-class classification logistic loss between input tensor x x x and target tensor y y y (containing 1 or -1).

The hypergraph convolutional operator from the "Hypergraph Convolution and Hypergraph Attention" paper. g., the j j j-th channel of the i i i-th sample in the batched input is a 1D tensor input [ i , j ] \text{input}[i, j] input [ i , j ]).

The graph neural network operator from the "Convolutional Networks on Graphs for Learning Molecular Fingerprints" paper. Applies Batch Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . BatchNorm1d module with lazy initialization of the num_features argument of the BatchNorm1d that is inferred from the input. The directional message passing neural network (DimeNet) from the "Directional Message Passing for Molecular Graphs" paper.

GAT  class GAT ( in_channels : int, hidden_channels : int, num_layers : int, out_channels : Optional [ int ] = None, dropout : float = 0. 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. The Gini coefficient from the "Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity" paper.

Combines one or more aggregators and transforms its output with one or more scalers as introduced in the "Principal Neighbourhood Aggregation for Graph Nets" paper. Performs GRU aggregation in which the elements to aggregate are interpreted as a sequence, as described in the "Graph Neural Networks with Adaptive Readouts" paper. Applies message normalization over the aggregated messages as described in the "DeeperGCNs: All You Need to Train Deeper GCNs" paper.

The Temporal Graph Network (TGN) memory model from the "Temporal Graph Networks for Deep Learning on Dynamic Graphs" paper.

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