Convolutional layers · GraphNeuralNetworks. jl Some of the most commonly used layers are the GCNConv and the GATv2Conv Multiple graph convolutional layers are typically stacked together to create a graph neural network model (see GNNChain) The table below lists all graph convolutional layers implemented in the GraphNeuralNetworks jl
GraphSAGE学习_sageconv-CSDN博客 GraphSAGE 是一个 inductive 的方法，在训练过程中，不会使用测试或者验证集的样本。 而 GCN 在训练过程中，会采集测试或者验证集中的样本，因此为 transductive 本节的内容假设 模型已经完成训练，参数已经固定。 包括： A G G R E G A T E k , ∀ k ∈ { 1 , , K } \mathrm {AGGREGATE}_k,\forall k\in\ {1, ,K\} AGGREGATEk ,∀k ∈ {1, ,K} W k , ∀ k ∈ { 1 , , K } \mathbf {W}^ {k},\forall k\in\ {1, ,K\} Wk,∀k ∈ {1, ,K}
Convolutional layers · GNNLux. jl Multiple graph convolutional layers are typically stacked together to create a graph neural network model (see GNNChain) The table below lists all graph convolutional layers implemented in the GNNLux jl It also highlights the presence of some additional capabilities with respect to basic message passing: