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The Adaptive Structure Aware Pooling operator from the "ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations" paper.

InstanceNorm1d module with lazy initialization of the num_features argument of the InstanceNorm1d that is inferred from the input.The Neural Fingerprint model from the "Convolutional Networks on Graphs for Learning Molecular Fingerprints" paper to generate fingerprints of molecules.

The dynamic edge convolutional operator from the "Dynamic Graph CNN for Learning on Point Clouds" paper (see torch_geometric. The Attentive FP model for molecular representation learning from the "Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism" paper, based on graph attention mechanisms. The powermean aggregation operator based on a power term, as described in the "DeeperGCN: All You Need to Train Deeper GCNs" paper.The anti-symmetric graph convolutional operator from the "Anti-Symmetric DGN: a stable architecture for Deep Graph Networks" paper. The FiLM graph convolutional operator from the "GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation" paper. The Gini coefficient from the "Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity" paper. Applies Batch Normalization over a 2D or 3D input as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . Memory based pooling layer from "Memory-Based Graph Networks" paper, which learns a coarsened graph representation based on soft cluster assignments.

The edge pooling operator from the "Towards Graph Pooling by Edge Contraction" and "Edge Contraction Pooling for Graph Neural Networks" papers. Applies a multi-layer Elman RNN with tanh ⁡ \tanh tanh or ReLU \text{ReLU} ReLU non-linearity to an input sequence. ConvTranspose3d module with lazy initialization of the in_channels argument of the ConvTranspose3d that is inferred from the input. Not only do girls here achieve academic excellence but they enjoy contributing to the school and wider community. 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.Applies the gated linear unit function G L U ( a , b ) = a ⊗ σ ( b ) {GLU}(a, b)= a \otimes \sigma(b) G LU ( a , b ) = a ⊗ σ ( b ) where a a a is the first half of the input matrices and b b b is the second half. The dynamic neighborhood aggregation operator from the "Just Jump: Towards Dynamic Neighborhood Aggregation in Graph Neural Networks" paper. The Weisfeiler Lehman (WL) operator from the "A Reduction of a Graph to a Canonical Form and an Algebra Arising During this Reduction" paper.