python基础 – networkx 绘图总结

目录

1、创建方式

2、基本参数

3、DiGraph-有向图

4、Graph-无向图

5、有向图和无向图互转

6、一些精美的图例子

1、创建方式

import networkx as nx import matplotlib.pyplot as plt # G = nx.random_graphs.barabasi_albert_graph(100, 1) # 生成一个BA无向图 # G = nx.MultiGraph() # 有多重边无向图 # G = nx.MultiDiGraph() # 有多重边有向图 # G = nx.Graph() # 无多重边无向图 G = nx.DiGraph() # 无多重边有向图 ... G.clear() # 清空图

2、基本参数

2.1 networkx 提供画图的函数

• draw（G，[pos,ax,hold]）
• draw_networkx(G，[pos,with_labels])
• draw_networkx_nodes(G,pos,[nodelist]) 绘制网络G的节点图
• draw_networkx_edges(G,pos[edgelist]) 绘制网络G的边图
• draw_networkx_edge_labels(G, pos[, …]) 绘制网络G的边图，边有label
• —有layout 布局画图函数的分界线—
• draw_circular(G, kwargs) Draw the graph G with a circular layout.
• draw_random(G, kwargs) Draw the graph G with a random layout.
• draw_spectral(G, kwargs) Draw the graph G with a spectral layout.
• draw_spring(G, kwargs) Draw the graph G with a spring layout.
• draw_shell(G, kwargs) Draw networkx graph with shell layout.
• draw_graphviz(G[, prog]) Draw networkx graph with graphviz layout.

2.2 networkx 画图参数

nx.draw(G,node_size = 30, with_label = False)

上例，绘制节点的尺寸为30，不带标签的网络图。

• node_size: 指定节点的尺寸大小(默认是300，单位未知，就是上图中那么大的点)
• node_color: 指定节点的颜色 (默认是红色，可以用字符串简单标识颜色，例如’r’为红色，’b’为绿色等，具体可查看手册)，用“数据字典”赋值的时候必须对字典取值（.values()）后再赋值
• node_shape: 节点的形状（默认是圆形，用字符串’o’标识，具体可查看手册）
• alpha: 透明度 (默认是1.0，不透明，0为完全透明)
• width: 边的宽度 (默认为1.0)
• edge_color: 边的颜色(默认为黑色)
• style: 边的样式(默认为实现，可选： solid|dashed|dotted,dashdot)
• with_labels: 节点是否带标签（默认为True）
• font_size: 节点标签字体大小 (默认为12)
• font_color: 节点标签字体颜色（默认为黑色）

2.3 networkx 画图函数里的一些参数

• pos(dictionary, optional): 图像的布局，可选择参数；如果是字典元素，则节点是关键字，位置是对应的值。如果没有指明，则会是spring的布局；也可以使用其他类型的布局，具体可以查阅networkx.layout
• arrows :布尔值，默认True; 对于有向图，如果是True则会画出箭头
• with_labels: 节点是否带标签（默认为True）
• ax：坐标设置，可选择参数；依照设置好的Matplotlib坐标画图
• nodelist：一个列表，默认G.nodes(); 给定节点
• edgelist：一个列表，默认G.edges();给定边
• node_size: 指定节点的尺寸大小(默认是300，单位未知，就是上图中那么大的点)
• node_color: 指定节点的颜色 (默认是红色，可以用字符串简单标识颜色，例如’r’为红色，’b’为绿色等，具体可查看手册)，用“数据字典”赋值的时候必须对字典取值（.values()）后再赋值
• node_shape: 节点的形状（默认是圆形，用字符串’o’标识，具体可查看手册）
• alpha: 透明度 (默认是1.0，不透明，0为完全透明)
• cmap：Matplotlib的颜色映射，默认None; 用来表示节点对应的强度
• vmin,vmax：浮点数，默认None;节点颜色映射尺度的最大和最小值
• linewidths：[None|标量|一列值];图像边界的线宽
• width: 边的宽度 (默认为1.0)
• edge_color: 边的颜色(默认为黑色)
• edge_cmap：Matplotlib的颜色映射，默认None; 用来表示边对应的强度
• edge_vmin,edge_vmax：浮点数，默认None;边的颜色映射尺度的最大和最小值
• style: 边的样式(默认为实现，可选： solid|dashed|dotted,dashdot)
• labels：字典元素，默认None;文本形式的节点标签
• font_size: 节点标签字体大小 (默认为12)
• font_color: 节点标签字体颜色（默认为黑色）
• node_size：节点大小
• font_weight：字符串，默认’normal’
• font_family：字符串，默认’sans-serif’

2.4 布局指定节点排列形式

pos = nx.spring_layout 

建立布局，对图进行布局美化，networkx 提供的布局方式有：

• circular_layout：节点在一个圆环上均匀分布
• random_layout：节点随机分布
• shell_layout：节点在同心圆上分布
• spring_layout： 用Fruchterman-Reingold算法排列节点（这个算法我不了解，样子类似多中心放射状）
• spectral_layout：根据图的拉普拉斯特征向量排列节

布局也可用pos参数指定，例如，nx.draw(G, pos = spring_layout(G)) 这样指定了networkx上以中心放射状分布.

3、DiGraph-有向图

G = nx.DiGraph() # 无多重边有向图 G.add_node(2) # 添加一个节点 G.add_nodes_from([3, 4, 5, 6, 8, 9, 10, 11, 12]) # 添加多个节点 G.add_cycle([1, 2, 3, 4]) # 添加环 G.add_edge(1, 3) # 添加一条边 G.add_edges_from([(3, 5), (3, 6), (6, 7)]) # 添加多条边 G.remove_node(8) # 删除一个节点 G.remove_nodes_from([9, 10, 11, 12]) # 删除多个节点 print("nodes: ", G.nodes()) # 输出所有的节点 print("edges: ", G.edges()) # 输出所有的边 print("number_of_edges: ", G.number_of_edges()) # 边的条数，只有一条边，就是（2，3） print("degree: ", G.degree) # 返回节点的度 print("in_degree: ", G.in_degree) # 返回节点的入度 print("out_degree: ", G.out_degree) # 返回节点的出度 print("degree_histogram: ", nx.degree_histogram(G)) # 返回所有节点的分布序列

输出>>

nodes: [2, 3, 4, 5, 6, 1, 7] edges: [(2, 3), (3, 4), (3, 5), (3, 6), (4, 1), (6, 7), (1, 2), (1, 3)] number_of_edges: 8 degree: [(2, 2), (3, 5), (4, 2), (5, 1), (6, 2), (1, 3), (7, 1)] in_degree: [(2, 1), (3, 2), (4, 1), (5, 1), (6, 1), (1, 1), (7, 1)] out_degree: [(2, 1), (3, 3), (4, 1), (5, 0), (6, 1), (1, 2), (7, 0)] degree_histogram: [0, 2, 3, 1, 0, 1] 

4、Graph-无向图

4.1 相关函数：

• all_neighbors(graph, node)：返回图中节点的所有邻居
• non_neighbors(graph, node)：返回图中没有邻居的节点
• common_neighbors(G, u, v)：返回图中两个节点的公共邻居
• non_edges(graph)：返回图中不存在的边

# 增加节点 G.add_node('a') # 添加一个节点1 G.add_nodes_from(['b', 'c', 'd', 'e']) # 加点集合 G.add_cycle(['f', 'g', 'h', 'j']) # 加环 H = nx.path_graph(10) # 返回由10个节点的无向图 G.add_nodes_from(H) # 创建一个子图H加入G G.add_node(H) # 直接将图作为节点 # print("non_edges: ", G.non_edges) # 返回图中不存在的边 print("all_neighbors: ") # 返回图中节点的所有邻居 a = nx.all_neighbors(G, "f") for i, node in enumerate(a): print(i, node) print("non_neighbors: ") # 返回图中没有邻居的节点 b = nx.non_neighbors(G, "f") for i, node in enumerate(b): print(i, node) print("common_neighbors: ") # 返回图中两个节点的公共邻居 c = nx.common_neighbors(G, "j", "g") for i, node in enumerate(c): print(i, node) print("non_edges: ") # 返回图中不存在的边 d = nx.non_edges(G) for i, node in enumerate(d): print(i, node) nx.draw(G, with_labels=True, node_color='red') plt.show()

 输出>>

all_neighbors: 0 g 1 j non_neighbors: 0 a 1 b 2 c 3 d 4 e 5 h 6 0 7 1 8 2 9 common_neighbors: 0 h 1 f non_edges: 0 (0, 'g') 1 (0, 'h') 2 (0, 1) 3 (0, 2) 4 (0, 'e') ...太多了

4.2 属性

• 属性诸如weight,labels,colors,或者任何对象，都可以附加到图、节点或边上。
• 对于每一个图、节点和边都可以在关联的属性字典中保存一个（多个）键-值对。
• 默认情况下这些是一个空的字典，但是可以增加或者是改变这些属性。

1、图的属性：

G = nx.Graph(day='Monday') # 可以在创建图时分配图的属性 print(G.graph) G.graph['day'] = 'Friday' # 也可以修改已有的属性 print(G.graph) G.graph['name'] = 'time' # 可以随时添加新的属性到图中 print(G.graph)

 输出>>

{'day': 'Monday'} {'day': 'Friday'} {'day': 'Friday', 'name': 'time'}

2、节点的属性：

G = nx.Graph(day='Monday') G.add_node(1, index='1th') # 在添加节点时分配节点属性 print(G.node(data=True)) G.node[1]['index'] = '0th' # 通过G.node[][]来添加或修改属性 print(G.node(data=True)) G.add_nodes_from([2, 3], index='2/3th') # 从集合中添加节点时分配属性 print(G.node(data=True))

输出>>

[(1, {'index': '1th'})] [(1, {'index': '0th'})] [(1, {'index': '0th'}), (2, {'index': '2/3th'}), (3, {'index': '2/3th'})]

3、边的属性：

G.add_edge(1, 2, weight=10) # 在添加边时分配属性 print(G.edges(data=True)) G.add_edges_from([(1, 3), (4, 5)], len=22) # 从集合中添加边时分配属性 print(G.edges(data='len')) G.add_edges_from([(3, 4, {'hight': 10}), (1, 4, {'high': 'unknow'})]) print(G.edges(data=True)) G[1][2]['weight'] =  # 通过G[][][]来添加或修改属性 print(G.edges(data=True))

输出>>

[(1, 2, {'weight': 10})] [(1, 2, None), (1, 3, 22), (4, 5, 22)] [(1, 2, {'weight': 10}), (1, 3, {'len': 22}), (1, 4, {'high': 'unknow'}), (3, 4, {'hight': 10}), (4, 5, {'len': 22})] [(1, 2, {'weight': }), (1, 3, {'len': 22}), (1, 4, {'high': 'unknow'}), (3, 4, {'hight': 10}), (4, 5, {'len': 22})]

5、有向图和无向图互转

G = nx.DiGraph() H = G.to_undirected() # 有向图转化成无向图-方法1 H = nx.Graph(G) # 有向图转化成无向图-方法2 F = H.to_directed() # 无向图转化成有向图-方法1 F = nx.DiGraph(H) # 无向图转化成有向图-方法2

6、一些精美的图例子

使用pydot时会遇见如下的错误：

FileNotFoundError: [WinError 2] “twopi” not found in path.

解决参考：

• https://www.cnblogs.com/shuodehaoa/p/8667045.html
• https://blog.csdn.net/darren2015zdc/article/details/
• https://blog.csdn.net/ciyiquan5963/article/details/
• http://www.downza.cn/soft/284938.html
• https://blog.csdn.net/weixin_/article/details/

1、 环形树状图

# 环形树状图 import os import pydot from networkx.drawing.nx_pydot import graphviz_layout def set_prog(prog="dot"): path = r'C:\Program Files (x86)\Graphviz 2.28\bin' # graphviz的bin目录 prog = os.path.join(path, prog) + '.exe' return prog G = nx.balanced_tree(3, 5) pos = graphviz_layout(G, prog=set_prog("twopi")) plt.figure(figsize=(8, 8)) nx.draw(G, pos, node_size=20, alpha=0.5, node_color="blue", with_labels=False) plt.axis('equal') plt.show()

 2、权重图

G = nx.Graph() G.add_edge('a', 'b', weight=0.6) G.add_edge('a', 'c', weight=0.2) G.add_edge('c', 'd', weight=0.1) G.add_edge('c', 'e', weight=0.7) G.add_edge('c', 'f', weight=0.9) G.add_edge('a', 'd', weight=0.3) elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] > 0.5] esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] <= 0.5] pos = nx.spring_layout(G) # positions for all nodes # nodes nx.draw_networkx_nodes(G, pos, node_size=700) # edges nx.draw_networkx_edges(G, pos, edgelist=elarge, width=6) nx.draw_networkx_edges(G, pos, edgelist=esmall, width=6, alpha=0.5, edge_color='b', style='dashed') # labels-标签 # nx.draw_networkx_labels(G, pos, font_size=20, font_family='sans-serif') plt.axis('off') plt.show()

3、Giant Component 

import pydot from networkx.drawing.nx_pydot import graphviz_layout def set_prog(prog="dot"): path = r'C:\Program Files (x86)\Graphviz 2.28\bin' # graphviz的bin目录 prog = os.path.join(path, prog) + '.exe' return prog pvals = [0.003, 0.006, 0.008, 0.015] # the range of p values should be close to the threshold plt.subplots_adjust(left=0, right=1, bottom=0, top=0.95, wspace=0.01, hspace=0.01) for i, p in enumerate(pvals): G = nx.binomial_graph(150, p) # 150 nodes pos = graphviz_layout(G, prog=set_prog("neato")) plt.subplot(2, 2, i + 1) plt.title("p = %6.3f" % (p,)) nx.draw(G, pos, with_labels=False, node_size=10) # 找出最大连通数的子图 Gcc = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True) G0 = Gcc[0] nx.draw_networkx_edges(G0, pos, with_labels=False, edge_color='r', width=6.0) # 画出其他连通数子图 [nx.draw_networkx_edges(Gi, pos, with_labels=False, edge_color='r', alpha=0.3, width=5.0) for Gi in Gcc[1:] if len(Gi) > 1] plt.show() 

4、Random Geometric Graph 随机几何图

G = nx.random_geometric_graph(200, 0.125) pos = nx.get_node_attributes(G, 'pos') # position is stored as node attribute data for random_geometric_graph # find node near center (0.5,0.5) dmin = 1 ncenter = 0 for n in pos: x, y = pos[n] d = (x - 0.5) 2 + (y - 0.5) 2 if d < dmin: ncenter = n dmin = d # color by path length from node near center p = dict(nx.single_source_shortest_path_length(G, ncenter)) plt.figure(figsize=(8, 8)) nx.draw_networkx_edges(G, pos, nodelist=[ncenter], alpha=0.4) nx.draw_networkx_nodes(G, pos, nodelist=list(p.keys()), node_size=80, node_color=list(p.values()), cmap=plt.cm.Reds_r) plt.xlim(-0.05, 1.05) plt.ylim(-0.05, 1.05) plt.show()

5、节点颜色渐变

G = nx.cycle_graph(24) pos = nx.spring_layout(G, iterations=200) nx.draw(G, pos, node_color=range(24), node_size=800, cmap=plt.cm.Blues) plt.show()

 6、边颜色渐变

G = nx.star_graph(20) pos = nx.spring_layout(G) # 布局为中心放射状 colors = range(20) nx.draw(G, pos, node_color='#A0CBE2', edge_color=colors, width=2, edge_cmap=plt.cm.Blues, with_labels=False) plt.show()

7、Atlas

import os import pydot import random from networkx.generators.atlas import graph_atlas_g from networkx.drawing.nx_pydot import graphviz_layout from networkx.algorithms.isomorphism.isomorph import graph_could_be_isomorphic as isomorphic def set_prog(prog="dot"): path = r'C:\Program Files (x86)\Graphviz 2.28\bin' # graphviz的bin目录 prog = os.path.join(path, prog) + '.exe' return prog def atlas6(): """ Return the atlas of all connected graphs of 6 nodes or less. Attempt to check for isomorphisms and remove. """ Atlas = graph_atlas_g()[0:208] # 208 # remove isolated nodes, only connected graphs are left U = nx.Graph() # graph for union of all graphs in atlas for G in Atlas: for n in [n for n in G if G.degree(n) == 0]: G.remove_node(n) U = nx.disjoint_union(U, G) # iterator of graphs of all connected components C = (U.subgraph(c) for c in nx.connected_components(U)) UU = nx.Graph() # do quick isomorphic-like check, not a true isomorphism checker nlist = [] # list of nonisomorphic graphs for G in C: # check against all nonisomorphic graphs so far if not iso(G, nlist): nlist.append(G) UU = nx.disjoint_union(UU, G) # union the nonisomorphic graphs return UU def iso(G1, glist): """Quick and dirty nonisomorphism checker used to check isomorphisms.""" if any(isomorphic(G1, G2) for G2 in glist): return True return False G = atlas6() print("graph has %d nodes with %d edges" % (nx.number_of_nodes(G), nx.number_of_edges(G))) print(nx.number_connected_components(G), "connected components") plt.figure(1, figsize=(8, 8)) pos = graphviz_layout(G, prog=set_prog("neato")) # color nodes the same in each connected subgraph C = (G.subgraph(c) for c in nx.connected_components(G)) for g in C: c = [random.random()] * nx.number_of_nodes(g) # random color... nx.draw(g, pos, node_size=40, node_color=c, vmin=0.0, vmax=1.0, with_labels=False) plt.show()

8、Club

import networkx.algorithms.bipartite as bipartite G = nx.davis_southern_women_graph() women = G.graph['top'] clubs = G.graph['bottom'] print("Biadjacency matrix") print(bipartite.biadjacency_matrix(G, women, clubs)) # project bipartite graph onto women nodes W = bipartite.projected_graph(G, women) print('') print("#Friends, Member") for w in women: print('%d %s' % (W.degree(w), w)) # project bipartite graph onto women nodes keeping number of co-occurence # the degree computed is weighted and counts the total number of shared contacts W = bipartite.weighted_projected_graph(G, women) print('') print("#Friend meetings, Member") for w in women: print('%d %s' % (W.degree(w, weight='weight'), w)) nx.draw(G, node_color="red") plt.show()

参考：

• 官方教程：https://networkx.github.io/documentation/stable/_downloads/networkx_reference.pdf
• 官方网站：https://networkx.github.io/documentation/latest/index.html
• 官方githu博客：http://networkx.github.io/
• https://blog.csdn.net/haoji007/article/details/
• https://blog.csdn.net/_/article/details/
• https://www.cnblogs.com/shuodehaoa/p/8667045.html
• https://blog.csdn.net/darren2015zdc/article/details/
• https://blog.csdn.net/ciyiquan5963/article/details/
• http://www.downza.cn/soft/284938.html
• https://blog.csdn.net/weixin_/article/details/