一下就可以得到这个subgraph里所有nodes的经过set transformer 计算之后的新的representation,取其中的dst nodes的representation 加个分类层就完事儿了. (2)gat,gatv2,dotgat in dgl 用到了attention的机制,没啥太多可说的pass。 (3)HGTCONV 虽然名字是叫异构图上的graph transformer,不过直接用在同构图上也没啥问题。
PGExplainer:来自“Parameterized Explainer for Graph Neural Network (https://arxiv.org/abs/2011.04573) ”论文的 PGExplainer 模型。 AttentionExplainer:使用基于注意力的 GNN(例如 GATConv、GATv2Conv 或 TransformerConv)产生的注意力系数作为边解释的解释器 CaptumExplainer:基于 Captum (https://captum.ai/) 的...
PGExplainer:来自“Parameterized Explainer for Graph Neural Network (https://arxiv.org/abs/2011.04573) ”论文的 PGExplainer 模型。 AttentionExplainer:使用基于注意力的 GNN(例如 GATConv、GATv2Conv 或 TransformerConv)产生的注意力系数作为边解释的解释器 CaptumExplainer:基于 Captum (https://captum.ai/) 的...
GMMConvfrom Montiet al.:Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs(CVPR 2017) FeaStConvfrom Vermaet al.:FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis(CVPR 2018) PointTransformerConvfrom Zhaoet al.:Point Transformer(2020) ...
🐛 Describe the bug I try to train a GNN network based on Transformer_Conv. It worked well on GPU RTX 3090 but failed on GPU RTX 4090. I also tested other GNN Convs like GCNConv or GATConv, GCNConv performed well but GATConv not. It seems...
🐛 Bug It seems that the torch_geometric.nn.TransformerConv layer will give an output which is of size out_channels * heads by default rather than just out_channels. Though this only happens when concat=True, it's a bit awkward to chain m...
output=conv(x,edge_index)# 输出卷积之后的特征表示矩阵print(output.data) 1.2.2 Edge Convolution的实现 在第二篇论文中,作者提出的卷积公式为 是一个多层感知机(MLP,前馈神经网络),还是化归到我们上面的一般化空域图卷积公式, 是求最大值函数, 是一个MLP,实现代码为 ...
TransformerConvfrom Shiet al.:Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification(CoRR 2020) [Example] SAGEConvfrom Hamiltonet al.:Inductive Representation Learning on Large Graphs(NIPS 2017) [Example1,Example2,Example3,Example4] ...
x = self.conv1(x, edge_index) x = F.relu(x) output = self.conv2(x, edge_index) return output gcn = GCN().to(device) optimizer_gcn = torch.optim.Adam(gcn.parameters(), lr=0.01, weight_decay=5e-4) criterion = nn.CrossEntropyLoss() ...
x = self.conv1(x, edge_index).relu() x = self.conv2(x, edge_index)returnx model = GNN(hidden_channels=64, out_channels=dataset.num_classes) model = to_hetero(model, data.metadata(), aggr='sum') 1. 2. 3. 4. 5. 6.