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. The Learnable Commutative Monoid aggregation from the "Learnable Commutative Monoids for Graph Neural Networks" paper, in which the elements are aggregated using a binary tree reduction with \(\mathcal{O}(\log |\mathcal{V}|)\) depth. The topology adaptive graph convolutional networks operator from the "Topology Adaptive Graph Convolutional Networks" paper. 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. Applies the Softplus function Softplus ( x ) = 1 β ∗ log ⁡ ( 1 + exp ⁡ ( β ∗ x ) ) \text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) Softplus ( x ) = β 1 ​ ∗ lo g ( 1 + exp ( β ∗ x )) element-wise.

Finally, we added full support for customization of aggregations into the SAGEConv layer — simply override its aggr argument and utilize the power of aggregation within your GNN. The PPFNet operator from the "PPFNet: Global Context Aware Local Features for Robust 3D Point Matching" 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 LINKX model from the "Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods" paper.Memory based pooling layer from "Memory-Based Graph Networks" paper, which learns a coarsened graph representation based on soft cluster assignments. The graph convolutional operator from the "Semi-supervised Classification with Graph Convolutional Networks" 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 gaussian mixture model convolutional operator from the "Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs" paper. Notably, all aggregations share the same set of forward arguments, as described in detail in the torch_geometric.

ConvTranspose1d module with lazy initialization of the in_channels argument of the ConvTranspose1d that is inferred from the input. The Adversarially Regularized Graph Auto-Encoder model from the "Adversarially Regularized Graph Autoencoder for Graph Embedding" paper. The label propagation operator, firstly introduced in the "Learning from Labeled and Unlabeled Data with Label Propagation" paper. The Graph Neural Network from the "Semi-supervised Classification with Graph Convolutional Networks" paper, using the GCNConv operator for message passing. The PointGNN operator from the "Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud" paper.