建立边，节点，特征

import dgl
import torch as th

（一）清除图

   g.clear()

（二）节点和边得数量

print("nodes",g.number_of_nodes())
print("edges",g.number_of_edges())

（二）类型和维度

print(g.node_attr_schemes())
print(g.edge_attr_schemes())

# {'x': Scheme(shape=(3,), dtype=torch.float32)}
# {'x': Scheme(shape=(4,), dtype=torch.float32)}

（三）删除节点和边特征

g.ndata.pop("x")
g.edata.pop("w")

（四）增加节点

g=dgl.DGLGraph()
g.add_nodes(10)
print("nodes",g.nodes())

（五）增加边

1.g.add_edge(i,j)

for i in range(1,4):
    g.add_edge(i,0)

print("edges",g.edges())

2.g.add_edges(src_list,dst_list)

src=list(range(5,8))
dst=[0]*3
g.add_edges(src,dst)

3.g.add_edges(src_tensors,dst_tensors)

src=th.tensor([8,9])
dst=th.tensor([0,0])
g.add_edges(src,dst)

4.g.add_edges(src_tensors,int)

src=th.tensor(list(range(1,10)))
g.add_edges(src,0)

 

（六）节点特征   g.ndata["x"] /g.nodes[:].data['x']

x=th.randn(10,3)
g.ndata["x"]=x

g.nodes[0].data["x"]=th.zeros(1,3)

g.nodes[[0,1,2]].data["x"]=th.zeros(3,3)

g.nodes[th.tensor([0,1,2])].data["x"]=th.zeros(3,3)

node.data["x"]

（七）边特征   g.edata["w"]/g.edges[:].data["w"]

w=th.randn(9,2)

g.edata["w"]=w

1.access edge set with IDs in integer, list, or integer tensor

g.edges[1].data["w"]=th.randn(1,2)--------------------int

g.edges[[0,1,2]].data["w"]=th.zeros(3,2)-------------list

g.edges[th.tensor([0,1,2])].data["w"]=th.zeros(3,2)---tensor

2.one can also access the edges by giving endpoints

g.edges[1,0].data["w"]=th.ones(1,2)----endpoints

g.edges[[1,2,3],[0,0,0]].data["w"]=th.ones(3,2)

（八）多变图  Multigraphs

  

g_multi=dgl.DGLGraph(multigraph=True)
g_multi.add_nodes(10)
g_multi.ndata["x"]=th.randn(10,2)

g_multi.add_edges(list(range(1,10)),0)

g_multi.add_edge(1,0)--1和0之间再加一条边

g_multi.edata["w"]=th.randn(10,2)

g_multi.edges[1].data["w"]=th.zeros(1,2)