比如在 :class:~dgl.nn.pytorch.conv.GraphConv 等conv模块中,DGL会检查输入图中是否有入度为0的节点。 当1个节点入度为0时, mailbox 将为空,并且聚合函数的输出值全为0, 这可能会导致模型性能不佳。但是,在 :class:~dgl.nn.pytorch.conv.SAGEConv 模块中,被聚合的特征将会与节点的初始特征拼接起来, ...
import torch.nn as nn import torch.nn.functional as F from dgl.data import * 构造一个两层的gnn模型 class SAGE(nn.Module): def __init__(self, in_feats, hid_feats, out_feats, dropout=0.2): super().__init__() self.conv1 = dglnn.SAGEConv( in_feats=in_feats, out_feats=hid_fea...
在SAGEConv中,子模块根据聚合类型而有所不同。这些模块是纯PyTorch NN模块,例如nn.Linear、nn.LSTM等。 构造函数的最后调用了reset_parameters()进行权重初始化。    1defreset_parameters(self):2"""重新初始化可学习的参数"""3gain = nn.init.calcul...
但是,在 :class:~dgl.nn.pytorch.conv.SAGEConv模块中,被聚合的特征将会与节点的初始特征拼接起来, forward()函数的输出不会全为0。在这种情况下,无需进行此类检验。 DGL NN模块可在不同类型的图输入中重复使用,包括:同构图、异构图(:ref:guide_cn-graph-heterogeneous)和子图块(:ref:guide_cn-minibatch)。
from dgl.nn.pytorch import GraphConv, SAGEConv, HeteroGraphConvfrom dgl.utils import expand_as_pairimport tqdmfrom collections import defaultdictimport torch as thimport dgl.nn as dglnnfrom dgl.data.utils import makedirs, save_info, load_infofrom sklearn.metrics import roc_auc_scoreimport gcgc...
import torch.nn.functional as Ffrom dgl.nn.pytorch import GraphConv, SAGEConv, HeteroGraphConvfrom dgl.utils import expand_as_pairimport tqdmfrom collections import defaultdictimport torch as thimport dgl.nn as dglnnfrom dgl.data.utils import makedirs, save_info, load_infofrom sklearn.metrics ...
load_graphsimport dgl.function as fnimport torchimport dglimport torch.nn.functional as Ffrom dgl.nn.pytorch import GraphConv, SAGEConv, HeteroGraphConvfrom dgl.utils import expand_as_pairimport tqdmfrom collections import defaultdictimport torch as thimport dgl.nn as dglnnfrom dgl.data.utils imp...
除了cuGraph DGL,cuGraph 还提供了 cuGraph 操作库,使 DGL 用户能够使用CuGraphSAGEConv,CuGraphGATConv和CuGraphRelGraphConv代替违约SAGEConv,GATConv和RelGraphConv模型。您也可以直接从导入 SAGEConv、GATConv 和 RelGraphConv 模型cugraph_dgl图书馆 在GNN 采样和训练中,主要的挑战是缺乏一种可以管理具有数十亿或数...
conv = dglnn.GraphConv(5, 3)y = conv(g, x) # Apply the graph convolution layer. DGL团队实现并发布了 15 种常用的 Tensorflow GNN 模块(更多模块正在路上),所有的模块都可以只用一行代码调用。 GraphConv from the Graph Convolutional Networks paper. GATConv from the Graph Attention Networks paper...
SAGEConv from the Inductive Representation Learning on Large Graphs paper (a.k.a. GraphSAGE). GINConv from the How Powerful are Graph Neural Networks paper. RelGraphConv from the Modeling Relational Data with Graph Convolutional Networks paper. ...