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The path integral based pooling operator from the "Path Integral Based Convolution and Pooling for Graph Neural Networks" paper.

The Graph Neural Network from the "Principal Neighbourhood Aggregation for Graph Nets" paper, using the PNAConv operator for message passing.The Graph Neural Network from the "Inductive Representation Learning on Large Graphs" paper, using the SAGEConv operator for message passing. The Graph Neural Network from the "Dynamic Graph CNN for Learning on Point Clouds" paper, using the EdgeConv operator for message passing.

The softmax aggregation operator based on a temperature term, as described in the "DeeperGCN: All You Need to Train Deeper GCNs" paper. The Efficient Graph Convolution from the "Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions" paper.The convolutional operator on \(\mathcal{X}\)-transformed points from the "PointCNN: Convolution On X-Transformed Points" paper. Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization. Applies instance normalization over each individual example in a batch of node features as described in the "Instance Normalization: The Missing Ingredient for Fast Stylization" paper.

The k-NN interpolation from the "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space" paper. 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 ). The fused graph attention operator from the "Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective" paper.InstanceNorm1d module with lazy initialization of the num_features argument of the InstanceNorm1d that is inferred from the input. The Adversarially Regularized Variational Graph Auto-Encoder model from the "Adversarially Regularized Graph Autoencoder for Graph Embedding" paper.

Applies layer normalization by subtracting the mean from the inputs as described in the "Revisiting 'Over-smoothing' in Deep GCNs" paper. A sampling algorithm from the "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space" paper, which iteratively samples the most distant point with regard to the rest points. Creates a criterion that measures the triplet loss given input tensors a a a, p p p, and n n n (representing anchor, positive, and negative examples, respectively), and a nonnegative, real-valued function ("distance function") used to compute the relationship between the anchor and positive example ("positive distance") and the anchor and negative example ("negative distance").The gaussian mixture model convolutional operator from the "Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs" paper. The dynamic neighborhood aggregation operator from the "Just Jump: Towards Dynamic Neighborhood Aggregation in Graph Neural Networks" paper.