class GCN(nn.Module): def __init__(self, in_size, hid_size, out_size): super().__init__() self.layers = nn.ModuleList() # two-layer GCN self.layers.append(dglnn.(in_size, hid_size, activation=F.relu)) self.layers.append(dglnn.GraphConv(hid_size, out_size)) self.dropout =...
这里我们采用 dgl 官方实现的 graphConv 算子进行邻居节点信息的聚合,不进行邻居节点的采样。 @ 欢迎关注微信公众号:算法全栈之路 class GCN(nn.Module): def __init__(self, in_size, hid_size, out_size): super().__init__() self.layers = nn.ModuleList() # two-layer GCN self.layers.append( ...
Module): def __init__(self, in_feats, h_feats, num_classes): super(GCN, self).__init__() self.conv1 = GraphConv(in_feats, h_feats) # 输入节点的特征维度in_feats=1433,隐藏层节点特征维度h_feats=16 self.conv2 = GraphConv(h_feats, num_classes) def forward(self, g, in_feat):...
DGL实际上已经封装好了GCN,即GraphConv,具体实现原理如下: 即GCN中,节点每次都聚合所有邻居的特征来生成自己新的特征,具体做法是对他们求加权和,而权重为\frac{1}{c_{ji}},而c_{ji}为两个节点度开根号的乘积。 因此,基于上述理论,我们搭建的GCNConv如下所示: def gcn_message_func(edges): w = edges....
在深度图学习(Deep Graph Learning, DGL)中,图卷积网络(Graph Convolutional Network, GCN)是一种强大的工具,用于处理图结构数据。GCN的核心思想...
super(GCN, self).__init__() self.conv1=GraphConv(in_feats, hidden_size) self.conv2=GraphConv(hidden_size, num_classes)defforward(self, g, inputs): h=self.conv1(g, inputs) h=torch.relu(h) h=self.conv2(g, h)returnhdeftrain(G, inputs, embed, labeled_nodes,labels): ...
初始化图卷积网络(GCN)。 参数: in_feats (int): 输入特征的维度。 h_feats (int): 隐藏层特征的维度。 num_classes (int): 输出类别的数量。 """ super(GCN, self).__init__() self.conv1 = GraphConv(in_feats, h_feats) # 第一层图卷积 ...
import dgl import torch import torch.nn as nn import torch.nn.functional as F from dgl.nn.pytorch import GraphConv class GCNWithEdgeFeatures(nn.Module): def __init__(self, in_feats, hidden_size, out_feats, edge_in_feats): super(GCNWithEdgeFeatures, self).__init__() self.conv1 = ...
class GCN(nn.Module): def __init__(self, in_size, hid_size, out_size): super().__init__() self.layers = nn.ModuleList() # two-layer GCN self.layers.append( dglnn.GraphConv(in_size, hid_size, activation=F.relu) ) self.layers.append(dglnn.GraphConv(hid_size, out_size)) ...
(5)基于GCN和DGL实现的图上 node 分类, 值得一看!!! 书接上文,在前面的几篇文章中,我们对在图上跑机器学习/深度学习模型有了一个大概的了解,并从 代码层面 一起 分别基于 DGL和 Graph Learn 框架实现了链接预测,节点分类与回归任务。下面让我们开始图上边(Edge) 的回归与分类任务的介绍吧～ ...