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...
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, -...
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')) Node types...
# 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...
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(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 ...
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 ...
# Nodes and edges are assigned integer IDs starting from zero and each type has its own counting. # To distinguish the nodes and edges of different types, specify the type name as the argument. print(pa_g.number_of_nodes('paper')) ...
在区块创建的阶段,dst nodes位于节点列表的最前面。通过索引[0:g.number_of_dst_nodes()]可以找到feat_dst。 确定feat_src和feat_dst之后,以上3种图类型的计算方法是相同的。 消息传递和聚合 import dgl.function as fnimport torch.nn.functional as Ffrom dgl.utils import check_eq_shapeif self._aggre_ty...
train_pos_g = dgl.graph((train_pos_u, train_pos_v), num_nodes=g.number_of_nodes()) train_neg_g = dgl.graph((train_neg_u, train_neg_v), num_nodes=g.number_of_nodes()) test_pos_g = dgl.graph((test_pos_u, test_pos_v), num_nodes=g.number_of_nodes()) test_neg...