import torch for node in range(g.number_of_nodes()): g.nodes[node]['feat'] = torch.rand((1, 5)) g.nodes[node]['label'] = 0 print(g.nodes[0]) print(g.nodes[1]) 输出: {'feat': tensor([[0.7551, 0.8906, 0.8504, 0.3191, 0.4822]]), 'label': 0} {'feat': tensor([[0.46...
{k: th.arange(g.number_of_nodes(k)) for k in g.ntypes}, sampler, batch_size=batch_size, shuffle=True, drop_last=False, num_workers=num_workers) for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader): # print(input_nodes) block = blocks[0].to(device) h = {k: x[k]...
import torch as th h1 = {'user' : th.randn((g.number_of_nodes('user'), 5))} h2 =conv(g, h1) print(h2.keys()) 官方代码已经写的非常通俗易懂了。 构图的话,简便直观的方法仍旧是以edge lists 进行构图,只不过需要带上source node,target node以及二者之间的edge type的类型,这里的类型使用字...
G=build_karate_club_graph()print("G中节点数 %d."%G.number_of_nodes())print("G中边数 %d."%G.number_of_edges()) visual(G)## 对 34 个节点做embeddingembed = nn.Embedding(34, 5)#34 nodes with embedding dim equal to 5print(embed.weight) G.ndata['feat'] =embed.weight#print out ...
print(pa_g.number_of_nodes('paper'))#如果规范边类型名称是唯一可区分的,则可以将其简化为边类型名称。print(pa_g.number_of_edges(('paper','written-by','author')))print(pa_g.number_of_edges('written-by'))## 获得论文#1 的作者print(pa_g.successors(1, etype='written-by'))...
num_nodes=author_g.number_of_nodes() train_mask=get_binary_mask(num_nodes, train_idx) val_mask=get_binary_mask(num_nodes, val_idx) test_mask=get_binary_mask(num_nodes, test_idx)print('dataset loaded') pprint({'dataset':'ACM','train': train_mask.sum().item() /num_nodes,'val'...
print(g.number_of_edges())print(g.number_of_nodes()) nx.draw(g.to_networkx(), node_size=50, node_color=[[.5, .5, .5,]]) plt.show() 1. 2. 3. 4. 在pagerank 中, 初始化每个节点初始值为 1/N, 将节点的出度作为节点的特征。
def load_one_graph(fn, data): # Create the graph using the edges and number of nodes edges = tuple(data['graph']['edges']) num_nodes = data['graph']['num_nodes'] dgl_graph = dgl.graph(edges, num_nodes=num_nodes) # Convert node attributes to PyTorch tensors and add them to ...
embed = nn.Parameter(torch.Tensor(g.number_of_nodes(ntype), self.embed_size)) nn.init.xavier_uniform_(embed, gain=nn.init.calculate_gain('relu')) self.embeds[ntype] = embed def forward(self, block=None): return self.embedsclass EntityClassify(nn.Module): def __init__(self, g, h...
print('Number of nodes for each graph element in the batch:', batched_graph.batch_num_nodes()) print('Number of edges for each graph element in the batch:', batched_graph.batch_num_edges()) # Recover the original graph elements from the minibatch ...