于是模型搭建如下: classRGCN(nn.Module):def__init__(self,in_channels,hidden_channels,out_channels):super(RGCN,self).__init__()self.conv1=RGCNConv(in_channels,hidden_channels,num_relations=num_relations,num_bases=30)self.conv2=RGCNConv(hidden_channels,out_channels,num_relations=num_relations...
R-GCN链接预测模型搭建如下: classRGCN_LP(nn.Module):def__init__(self,in_channels,hidden_channels,out_channels):super(RGCN_LP,self).__init__()self.conv1=RGCNConv(in_channels,hidden_channels,num_relations=num_relations,num_bases=30)self.conv2=RGCNConv(hidden_channels,out_channels,num_relat...
fromtorch_geometric.nnimportGCNConvclassGCN(torch.nn.Module):""" 2层GCN参数说明---nfeat : 输入特征的维度nhid : 隐藏神经元的数量nclass : 输出神经元的数量,也即类别的数量dropout : dropout中的概率with_bias: 是否带有偏置项"""def__init__(self,nfeat,nhid,nclass,dropout=0.5,with_bias=True)...
R-GCN链接预测模型搭建如下:class RGCN_LP(nn.Module): def __init__(self, in_channels, hidden_channels, out_channels): super(RGCN_LP, self).__init__() self.conv1 = RGCNConv(in_channels, hidden_channels, num_relations=num_relations, num_bases=30) self.conv2 = RGCNConv(hidden_...
让我们以GCNConv为例. 一般来说, 它的结构如下: from torch_geometric.nn import MessagePassing class GCNConv(MessagePassing): def __init__(self, in_channels, out_channels): # Initialize the class, call "super" specifying your aggregations super(GCNConv, self).__init__(aggr='add') def forw...
self.conv2=GCNConv(16,dataset.num_classes)defforward(self,data): x, edge_index = data.x, data.edge_index x = self.conv1(x, edge_index) x = F.relu(x) x = F.dropout(x, training=self.training) x = self.conv2(x, edge_index)returnF.log_softmax(x, dim=1) ...
PyG框架实现GCN,代码基本上直接使用官方文档的例子,然后是使用原生Pytorch实现GCN和Linear GNN,模型任务基于论文引用数据Cora数据集,用于实现半监督节点分类任务,具体代码和说明可以参见Github。 对于Cora数据集构建模型的输入,处理代码如下 content_path="./cora/cora.content"cite_path="./cora/cora.cites"# 读取文本...
GCN链接预测模型搭建如下: class GCN_LP(torch.nn.Module): def __init__(self, in_channels, hidden_channels, out_channels): super().__init__() self.conv1 = GCNConv(in_channels, hidden_channels) self.conv2 = GCNConv(hidden_channels, out_channels) def encode(self, x, edge_index): x...
GCNConv从ChebConv出发, 假设: K=1 (线性近似): v∗w=∑k=01θkTk(L~)x=θ0x+θ1L~x λmax=2: v∗w=θ0x+θ1(L−I)x=θ0x−θ1D−1/2AD1/2x θ0=−θ1=θ: v∗w=(I+D−1/2AD1/2)xθ 重新归一化 θ: A~:=I+A D~ii:=∑jA~ij 因此我们有: v∗w...
class GCN(torch.nn.Module): def __init__(self, feature, hidden, classes): super(GCN, self).__init__() self.conv1 = GCNConv(feature, hidden) self.conv2 = GCNConv(hidden, classes) def forward(self, data): x, edge_index = data.x, data.edge_index ...