networkx使用

画有向图

from matplotlib import pyplot as plt
import networkx as nx
# directed  graph
G=nx.DiGraph()
G.add_nodes_from([1,2,3])
G.add_edges_from([(1,2),(1,3)])
nx.draw_networkx(G)
plt.show()

调整节点之间的间距

    pos = nx.spring_layout(G,k=0.15,iterations=20)
    # k controls the distance between the nodes and varies between 0 and 1
    # iterations is the number of times simulated annealing is run
    # default k =0.1 and iterations=50

from matplotlib import pyplot as plt
import networkx as nx
# directed  graph
G=nx.DiGraph()

G.add_edges_from([(1, 2), (1, 3)])
# G.add_node(1)
G.add_edge(1, 2)
G.add_node("spam")        # adds node "spam"
G.add_nodes_from("spam")  # adds 4 nodes: 's', 'p', 'a', 'm'
G.add_edge(3, 'm')

G.remove_node(3)
G.add_node(1, time='5pm')
print(G.nodes[1])  # 显示{'time': '5pm'}
# print(list(G.nodes))
#
# print(list(G.edges))

# print(G.graph)
# G.nodes[1]['room'] = 714
# G.add_edges_from([(3, 4), (4, 5)], color='red')
# print(G[4][3]['color'])

from matplotlib import pyplot as plt
import networkx as nx
# directed  graph
DG = nx.DiGraph()
DG.add_weighted_edges_from([(1, 2, 0.5), (3, 1, 0.75)])
print(DG.out_degree(3, weight='weight'))   #从该点出去的边的权重
print(DG.in_degree(3, weight='weight'))  #进入该点的边的权重
print( DG.degree(1, weight='weight'))  # 与该点相连的所有边的权重和
DG[1][2]['color']='red'
nx.draw(DG, with_labels=True, font_weight='bold')
plt.show()