GatedGCN是一种用来处理带有edge feature常见的GNN conv方法，其计算过程和框图如图所示. $$ \mathrm {h}_ {i}^ {l+1}=A^l h_ {i}^ {l}+\Sigma_ {j \in \mathrm {N}_ {i}} \hat {e}_ {ij}^l \odot B^l h_ {j}^ {l}$$ 其中： \hat {e}_ {ij}^ {l}=\sigma (e_ {ij}^ {l+1}) \div\left (\Sigma_ {j\in \mathrm {N}_ {i}} \sigma\left (e_ {ij}^ {l+1}\right)+\varepsilon\right)

2017年11月20日 · Numerical results show that the proposed graph ConvNets are 3-17% more accurate and 1.5-4x faster than graph RNNs. Graph ConvNets are also 36% more accurate than variational (non-learning) techniques. Finally, the most effective graph ConvNet architecture uses gated edges and residuality.

GGNN是一种基于 GRU 的经典的空间域message passing的模型。 message passing的通用框架共包含三部分操作：信息传递操作 (M)，更新操作 (U)，读取操作 (R)。 从如下公式中可以看出，节点v的t+1时刻的embedding m，由其当前时刻的embedding，以及其邻居节点的当前时刻embedding，和二者的交互的边信息所决定。 在全图的信息传递过程中，采用的是GRU的原理。 传播模型如下图所示，其中式 (1)， h_v^1 为节点v的初始隐向量，为D维的向量，当节点输入 …

GatedGCN: Residual Gated Graph ConvNets. An Experimental Study of Neural Networks for Variable Graphs (Xavier Bresson and Thomas Laurent) https://arxiv.org/pdf/1711.07553v2.pdf. """ class GatedGCNLayer(nn.Module): """ Param: []

By incorporating the weighted fusion of pixel-level CNN and superpixel-level Spectral-Spatial Gated GCN, the proposed approach mitigates the salt-and-pepper noise commonly encountered in CNNs and compensates for the difficulty of capturing fine-grained information in superpixel-level GCNs for enhancing hyperspectral image classification.

2021年1月19日 · 本报告总结了我们为什么需要图神经网络 (Graph Neural Networks\GNN)，并分析了一种特定的模型体系结构——Gated Graph Convolutional Network（门控图卷积网络）。 图表示学习是在低维嵌入中有效总结图形结构的任务。 随着深度学习的兴起，研究人员提出了各种涉及使用神经网络进行图表示学习的架构。 我们把这样的架构称为图神经网络。 为什么我们需要图神经网络（GNN）？ 在谈论 GNN 细节之前，我们首先要了解这样做的背后动机。 有人可能会 …

2020年8月18日 · Gated Graph Convolutional Network The GatedGCN architecture is an anisotropic message-passing-based GNN that employs residual connections, batch normalization, and edge gates. The given figure summarizes each layer of the GatedGCN network.

2015年11月17日 · In this work, we study feature learning techniques for graph-structured inputs. Our starting point is previous work on Graph Neural Networks (Scarselli et al., 2009), which we modify to use gated recurrent units and modern optimization techniques and then extend to …

We provide four versions of Graph Neural Networks: Gated Graph Neural Networks (one implementation using dense adjacency matrices and a sparse variant), Asynchronous Gated Graph Neural Networks, and Graph Convolutional Networks (sparse).

2021年12月11日 · Unlike these three models, our proposed model uses a gated graph convolutional network to obtain sentence embedding. Compared to PCNN encoder, our proposed gated GCN can not only capture the dependency information in a sentence but also aggregate both position-based embeddings and entity-based embeddings.