conv =Edge_GATConv(1, 2, 1) # emb(in), emb(out), edge_dim x = conv(x, edge_index, edge_attr) Here we let in_channels = 1 & out_channels= 2, so we increase the dim from 1 to 2. And the edge_dim = 1 means each edge value's dim = 1. When we inputxandedge_index,...
torch_geometric gatconv定义 torch_geometric中的GATConv(Graph Attention Network)模块是用于图形数据的一种卷积神经网络模块。它基于注意力机制,可以对图中节点的特征进行编码和聚合。 GATConv在每个节点处计算出它周围节点的重要性权重,然后使用这些权重聚合邻居节点的特征,最终将所有邻居的信息进行整合,生成一个节点的...
PyG用torch_geometric.data.Data保存图结构的数据,导入的data(这个data指的是你导入的具体数据,不是前面那个torch_geometric.data)在PyG中会包含以下属性: data.x:图节点的属性信息,比如社交网络中每个用户是一个节点,这个x可以表示用户的属性信息,维度为[num_nodes, num_node_features] data.edge_index:COO格式...
Source File: test_gcn_conv.py From pytorch_geometric with MIT License 5 votes def test_gcn_conv_with_sparse_input_feature(): x = torch.sparse_coo_tensor(indices=torch.tensor([[0, 0], [0, 1]]), values=torch.tensor([1., 1.]), size=torch.Size([4, 16])) edge_index = torch...
edge_index: based on the directed graph, we get x1->x0, x2->x0, x3->x0. It should be written like, row: [1, 2, 3] & col: [0, 0, 0] Initializing and calling it is straightforward conv = GATConv(1, 2) # emb(in), emb(out) ...