GNN的输出函数一般是一个简单的可微函数g(\boldsymbol{h}_{v},l_{v})。一般来说,输出函数同样使用线性映射或神经网络实现。 对于node-focused任务来说,每个节点的输出是相互独立的,一般通过将最终的节点表示映射为相应的输出来实现:o_{v}=g(\boldsymbol{h}_{v}(T),l_{v})。
GNN是一种可以将图或图中的节点映射为某种向量表示的神经网络。 (1)为了使GNN能够用于处理序列问题,本文提出了Gated Graph Sequence Neural Network(GGS-NN)。 (2)针对GNN的训练问题,本文也做了相应的改进。 实验证明,本文所提出了GGS-NN在bAbI任务上取得了较好的效果。 文章:https://arxiv.org/abs/1511.05493...
Graph neural network(GNN) has become more widely used in recommendation systems in recent years, because of their ability to naturally integrate node information and topology. However, most of the current recommendation methods based on graph structure only focus on a single recommendation domain (...
A novel approach using genetic neural networks (GNN), which apply a genetic algorithm for weight and bias optimization instead of the traditional backpropagation method, has shown promising results in wind velocity prediction [42, 43]. Enhancing ANN training with particle swarm optimization (PSO) ha...
As far as we know, the proposed IEGN model is the first to apply the gated network to control the dynamic propagation of information in the GNN, apply the time factor to track the dynamic evolution of user interests, and finally complete the prediction of user preferences. The contribution ...
对于具有同构领域的实体, GNN会学习到具有相同特征向量的实体的相同表示,这些特征向量表示相应的邻居。 在某些情况下,如果两个实体有着同构的邻居结构,并且先预对齐部分的邻居结构,GNN也可以捕获到要对齐的其他邻居的相似性。 但是在三角形的图结构中,两个不同的实体可能会学到一样的表示,虽然R-GCN在公式中加入了...
ProtGNN [27] combines proto-type learning with GNNs and uses the comparison results between the input and some learned prototypes in the latent space as the model's prediction. In addition to applications in the above general domain, GNNs have also been applied to other downstream domains, incl...
A novel approach using genetic neural networks (GNN), which apply a genetic algorithm for weight and bias optimization instead of the traditional backpropa- gation method, has shown promising results in wind velocity prediction [42, 43]. Enhancing ANN training with particle swarm optimization (PSO...
但是如果要让GNN layer能够同时传递node和edge的representations,从而使得下一层的edges直接使用上一层的edges的representations作为input, 要保证每层的graph 结构是完全一样的,这是不可能的,除非像原论文一样直接对全图做卷积计算(这篇论文还是做图级别的任务),如果是node level的,第一层和第二层的拓扑结构不同,在...
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 公式 hil+1=Alhil+Σj∈Nie^ijl⊙Blhjl 其中: e^ijl=σ(eijl+1)÷(Σj∈Niσ(eijl+1)+ε) eijl+1=Dlhil+Elhjl+Cleijl 计算框图 DGL实现 DGL目前已经实现了GateGCN: import torch import torch.nn as nn ...