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The differentiable pooling operator from the "Hierarchical Graph Representation Learning with Differentiable Pooling" paper.

The Adversarially Regularized Variational Graph Auto-Encoder model from the "Adversarially Regularized Graph Autoencoder for Graph Embedding" paper. Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.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 . The chebyshev spectral graph convolutional operator from the "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering" paper.

The local extremum graph neural network operator from the "ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations" paper.Creates a criterion that measures the loss given inputs x 1 x1 x 1, x 2 x2 x 2, two 1D mini-batch or 0D Tensors, and a label 1D mini-batch or 0D Tensor y y y (containing 1 or -1). mathrm{top}_k\) pooling operator from the "Graph U-Nets", "Towards Sparse Hierarchical Graph Classifiers" and "Understanding Attention and Generalization in Graph Neural Networks" papers. InstanceNorm3d module with lazy initialization of the num_features argument of the InstanceNorm3d that is inferred from the input. The Graph Neural Network Force Field (GNNFF) from the "Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture" paper.

The graph attentional propagation layer from the "Attention-based Graph Neural Network for Semi-Supervised Learning" paper. The Graph Neural Network from the "Inductive Representation Learning on Large Graphs" paper, using the SAGEConv operator for message passing.The DistMult model from the "Embedding Entities and Relations for Learning and Inference in Knowledge Bases" paper.