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The label embedding and masking layer from the "Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification" paper. The k-NN interpolation from the "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space" paper. The path integral based convolutional operator from the "Path Integral Based Convolution and Pooling for Graph Neural Networks" paper. The equilibrium aggregation layer from the "Equilibrium Aggregation: Encoding Sets via Optimization" paper. The P(ropagational)MLP model from the "Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs" paper.

The GENeralized Graph Convolution (GENConv) from the "DeeperGCN: All You Need to Train Deeper GCNs" paper. For example, mean aggregation captures the distribution (or proportions) of elements, max aggregation proves to be advantageous to identify representative elements, and sum aggregation enables the learning of structural graph properties ( Xu et al.The heterogeneous edge-enhanced graph attentional operator from the "Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction" paper. Applies local response normalization over an input signal composed of several input planes, where channels occupy the second dimension.

Not only do girls here achieve academic excellence but they enjoy contributing to the school and wider community. Aggregation functions play an important role in the message passing framework and the readout functions of Graph Neural Networks.The Frequency Adaptive Graph Convolution operator from the "Beyond Low-Frequency Information in Graph Convolutional Networks" paper. ConvTranspose3d module with lazy initialization of the in_channels argument of the ConvTranspose3d that is inferred from the input. The Graph Neural Network from the "Semi-supervised Classification with Graph Convolutional Networks" paper, using the GCNConv operator for message passing. The LINKX model from the "Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods" paper. Importantly, MultiAggregation provides various options to combine the outputs of its underlying aggegations ( e.

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