以下是一个简化的Dynamic GNN模型示例代码框架(使用PyTorch和PyTorch Geometric库): python import torch import torch.nn.functional as F from torch_geometric.nn import GCNConv, GATConv from torch_geometric.data import Data from torch.nn.utils.rnn import pad_sequence class DynamicGNN(torch.nn.Module): ...
during training, we want to sample negative edges on-the-fly with# a ratio of 2:1.# We can make use of the `loader.LinkNeighborLoader` from PyG:fromtorch_geometric.loaderimportLinkNeighborLoader#每次
from torch import nn from torch.functional import F from torch.optim import Adam from torch_geometric.nn import MetaLayer, MessagePassing from models import OGN, varOGN, make_packer, make_unpacker, get_edge_index aggr = 'add' hidden = 300 test = '_l1_' #This test applies an explicit bo...
Unable to use torch geometric with deepsnap [Unable to cast Python instance to C++ type] Example code: import networkx as nx G = nx.DiGraph() G.add_node(0, node_type='n1', node_label=1, node_feature=torch.Tensor([0.1, 0.2, 0.3, 0.7])) G.add_node(1, node_type='n1', node_la...