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The ClusterGCN graph convolutional operator from the “Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks” paper The Principal Neighbourhood Aggregation graph convolution operator from the “Principal Neighbourhood Aggregation for Graph Nets” paper The local extremum graph neural network operator from the “ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations” paper, which finds the importance of nodes with respect to their neighbors using the difference operator: The hypergraph convolutional operator from the “Hypergraph Convolution and Hypergraph Attention” paper The Point Transformer layer from the “Point Transformer” paper The (translation-invariant) feature-steered convolutional operator from the “FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis” paper The PPFNet operator from the “PPFNet: Global Context Aware Local Features for Robust 3D Point Matching” paper \[\mathbf\)-transformed points from the “PointCNN: Convolution On X-Transformed Points” paper Modules ( ) – A list of modules (withĬlass Linear ( in_channels : int, out_channels : int, bias : bool = True, weight_initializer : Optional = None, bias_initializer : Optional = None ) ¶Īpplies a linear tranformation to the incoming data Input_args ( str) – The input arguments of the model.

Heterogeneous Graph Neural Network Operatorsįrom torch.nn import Linear, ReLU, Dropout from torch_geometric.nn import Sequential, GCNConv, JumpingKnowledge from torch_geometric.nn import global_mean_pool model = Sequential ( 'x, edge_index, batch', , 'x1, x2 -> xs' ), ( JumpingKnowledge ( "cat", 64, num_layers = 2 ), 'xs -> x' ), ( global_mean_pool, 'x, batch -> x' ), Linear ( 2 * 64, dataset.