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Applies pair normalization over node features as described in the "PairNorm: Tackling Oversmoothing in GNNs" paper. The Graph Neural Network from the "Semi-supervised Classification with Graph Convolutional Networks" paper, using the GCNConv operator for message passing. The DimeNet++ from the "Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules" paper. The chebyshev spectral graph convolutional operator from the "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering" 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.

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The Gini coefficient from the "Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity" paper. The Node2Vec model from the "node2vec: Scalable Feature Learning for Networks" paper where random walks of length walk_length are sampled in a given graph, and node embeddings are learned via negative sampling optimization.

Performs Deep Sets aggregation in which the elements to aggregate are first transformed by a Multi-Layer Perceptron (MLP) \(\phi_{\mathbf{\Theta}}\), summed, and then transformed by another MLP \(\rho_{\mathbf{\Theta}}\), as suggested in the "Graph Neural Networks with Adaptive Readouts" paper. First, aggregations can be resolved from pure strings via a lookup table, following the design principles of the class-resolver library, e.The soft attention aggregation layer from the "Graph Matching Networks for Learning the Similarity of Graph Structured Objects" paper. Applies Graph Size Normalization over each individual graph in a batch of node features as described in the "Benchmarking Graph Neural Networks" paper.

The ARMA graph convolutional operator from the "Graph Neural Networks with Convolutional ARMA Filters" paper.

The relational graph convolutional operator from the "Modeling Relational Data with Graph Convolutional Networks" 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 .

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