g.number_of_nodes()#节点数量g.number_of_edges()#边数量g.nodes()#查看节点g.edges()#查看边g.add_nodes()#添加节点g.add_edges()#添加边g.in_degrees([0,1])#查看节点0和节点1的入度。g.out_degrees([0,1])#查看节点0和节点1的出度。g.ndata.update({'id': torch.arange(5),'in_degree...
{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]...
# 查看节点数量 G.number_of_nodes() # 查看边的数量 G.number_of_edges() # 查看节点的度 G.degree(node_id) # 度 = 出度 + 入度 G.out_degree(node_id) # 出度 G.in_degree(node_id) # 入度 常用算法api nx.connected_components(G) # 连通图 nx.pagerank(G) # PageRank nx.shortest_path...
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 ...
super(HeteroRGCN, self).__init__()#Use trainable node embeddings as featureless inputs.embed_dict ={ntype : nn.Parameter(torch.Tensor(G.number_of_nodes(ntype), in_size))forntypeinG.ntypes}forkey, embedinembed_dict.items(): nn.init.xavier_uniform_(embed) ...
nodes('item') (tensor([0, 1, 2, 3, 4]), tensor([0, 1, 2, 3])) 某类节点数量 g.number_of_nodes('user') 5 g.num_nodes('user') 5 节点属性赋值 embedding_size = 10 u_feat = torch.randn((g.number_of_nodes('user'), embedding_size)) u_feat tensor([[ 0.8453, 0.5088, -...
(G.number_of_nodes() - 1): # 不包括刚刚添加的全局节点 G.add_edges(node_id, global_node_id) # 验证全局连接点是否已成功添加到图中 print(f"Number of nodes: {G.number_of_nodes()}") print(f"Number of edges: {G.number_of_edges()}") print(f"Edges of global node {global_node_...
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。
print("G中节点数 %d."% G.number_of_nodes()) # 34 print("G中边数 %d."% G.number_of_edges()) # 156 1. 2. 3. 转为networkX进行可视化 AI检测代码解析 defvisual(G):#可视化nx_G =G.to_networkx().to_undirected() pos= nx.kamada_kawai_layout(nx_G)## 生成节点位置nx.draw(nx_G,...
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 ...