apply_nodes 方法 copy_u以及copy_e dgl库之高级用法dgl.DGLGraph.update_all dgl.function.copy_u dgl.function.sum update_all 回到顶部 dgl创建一个图 #创建一个dglg = dgl.DGLGraph()#该dgl图一共有6个点g.add_nodes(6)#添加边[0,1],[0,2]是有向边。这里一共添加了5条边g.add_edges([0, ...
g.add_edges(src, dst) # edges are directional in DGL; make them bi-directional g.add_edges(dst, src) return g 输出创建的节点和边的数量 G = build_karate_club_graph() print('We have %d nodes.' % G.number_of_nodes()) print('We have %d edges.' % G.number_of_edges()) >>>We...
g3=dgl.DGLGraph(g_nx)## 方式4:加边 (没有上面的方法高效)g4 =dgl.DGLGraph() g4.add_nodes(10)#添加节点数量 该方法第二个参数是添加每个节点的特征。## 加入边foriinrange(1,5):#一条条边添加g4.add_edge(i,0) src= list(range(5,8));dst = [0]*3#使用list批量添加g4.add_edges(...
g3=dgl.DGLGraph(g_nx)## 方式4:加边 (没有上面的方法高效)g4 =dgl.DGLGraph() g4.add_nodes(10)#添加节点数量 该方法第二个参数是添加每个节点的特征。## 加入边foriinrange(1,5):#一条条边添加g4.add_edge(i,0) src= list(range(5,8));dst = [0]*3#使用list批量添加g4.add_edges(...
g.add_edges(dst,src) returng G=build_karate_club_graph() print('We have %d nodes.'%G.number_of_nodes()) print('We have %d edges.'%G.number_of_edges()) importnetworkxasnx nx_G=G.to_networkx().to_undirected() # Kamada-Kawaii layout usually looks pretty for arbitrary graphs ...
g.add_nodes(n_atoms) bond_src = [] bond_dst = [] for i, bond in enumerate(mol.GetBonds()): a1 = bond.GetBeginAtom() a2 = bond.GetEndAtom() begin_idx = a1.GetIdx() end_idx = a2.GetIdx() bond_src.append(begin_idx) bond_dst.append(end_idx) bond_src.append(end_idx) ...
()g=DGLGraph()node_feats=[]fori,atominenumerate(mol.GetAtoms()):assert i==atom.GetIdx()node_feats.append(atom_features(atom))g.add_nodes(n_atoms)bond_src=[]bond_dst=[]fori,bondinenumerate(mol.GetBonds()):a1=bond.GetBeginAtom()a2=bond.GetEndAtom()begin_idx=a1.GetIdx()end_...
Mxnet:import dgl import mxnet as mx g = dgl.DGLGraph() g.add_nodes(5) # add 5 nodes...
x = F.relu(self.conv1(g, features)) x = self.conv2(g, x) return x #创建一个包含5个节点和2个特征维度的图 g = dgl.DGLGraph() g.add_nodes(5) g.ndata['feat'] = torch.randn(5, 2) #添加6条边 src = [0, 1, 2, 3, 4, 5] dst = [1, 2, 3, 4, 0, 2] g.add_...
图卷积网络 Graph Convolutional Network (GCN) 告诉我们将局部的图结构和节点特征结合可以在节点分类任务...