graph-tool 练习

如何使用graph-tool模块，如何导入？如何使用graph，使用其算法？

如何使用Boost Graph库，安装，测试？

1 创建和操纵图

如何创建空图？

g = Graph()

如何精准的创建有向图和无向图？

ug = Graph(directed=False)

如何切换有向和无向？

ug.set_directed(False)

如何查询图的有向和无向属性？

assert(ug.is_directed() == False)

如何通过一个已有的图创建新图？

g1 = Graph()

g2 = Graph(g1)

如何添加顶点？

v1 = g.add_vertex()

v2 = g.add_vertex()

如何创建边？

e = g.add_edge(v1， v2)

如何浏览显示已有的图？

graph_draw(g， vertex_text=g.vertex_index， vertex_font_size=18，output_size=(200， 200)， output="two-nodes.png")

如何获得顶点的出度？

print(v1.out_degree())

怎么返回一条边的source和target？

print(e.source()， e.target())

如何创建顶点，创建指定数量的顶点？

vlist = g.add_vertex(10)

print(len(list(vlist)))

如何获得顶点的索引？

v = g.add_vertex()

print(g.vertex_index[v])

print(int(v))

怎么将顶点和边删除？fast == True选项如何使用？set_fast_edge_removal()如何使用？

g.remove_edge(e)

g.remove_vertex(v2)

如何通过索引获得顶点？

v = g.vertex(8)

如何通过索引获得边？

g.add_edge(g.vertex(2)， g.vertex(3))

e = g.edge(2， 3)

如何显示边的索引？

e = g.add_edge(g.vertex(0)， g.vertex(1))

print(g.edge_index[e])

1.1 遍历顶点和边

1.1.1 遍历所有顶点或边

如何遍历图所有的顶点或边？

vertices()

edges()

for v in g.vertices()：
print(v)
for e in g.edges()：
print(e)

1.1.2 遍历一个顶点的neighbourhood

如何遍历顶点的出/入边以及出/入邻接点？

out_edges()

in_edges()

out_neighbours()

in_neighbours()

from itertools import izip
for v in g.vertices()：
for e in v.out_edges()：
print(e)
for w in v.out_neighbours()：
print(w)

# the edge and neighbours order always match
for e，w in izip(v.out_edges()， v.out_neighbours())：
assert(e.target() == w)

2 属性映射

什么是属性映射？有哪几种类型？由哪个类操作？属性映射的值得类型有哪几种？

一种将额外信息与顶点、边或图本身相关联的方式。

顶点、边和图。

PropertyMap类

bool、int16_t、int32_t、int64_t、double、long double、string、vector bool

vector uint8_t、vector int16_t、vector int32_t、vector int64_t、vector double

vector long double、vector string、python::object

如何为图创建新的属性映射？

new_vertex_property()

new_edge_property()

new_graph_property()

如何访问属性映射？

通过顶点或边的描述符或图本身，来访问该值（属性映射描述符[顶点、边或图]）

vprop_double = g.new_vertex_property("double")

顶点的属性映射

vprop_double[g.vertex(10)] = 3.1416

.

vprop_vint = g.new_vertex_property("vector<int>")

顶点的属性映射

vprop_vint[g.vertex(40)] = [1, 3, 42, 54]

.

eprop_dict = g.new_edge_property("object")

边的属性映射

eprop_dict[g.edges().next()] = {"foo": "bar", "gnu": 42}

.

gprop_bool = g.new_graph_property("bool")

图的属性映射

gprop_bool[g] = True

属性映射访问的其他形式？

vprop_double.get_array()[:] = random(g.num_vertices())

get_array()方法

vprop_double.a = random(g.num_vertices())

a属性

2.1 内部属性映射

什么是内部属性映射？

被复制并和图一起被保存到一个文件，属性被内在化

怎么使用内部属性映射？

属性映射必须有一个唯一的名称，相当于一个类型，可以产生具体的实例，即具体的属性

vertex_properties

vp

edge_properties

ep

graph_properties

gp

区分类型，名字和值！！！

>>> gprop = g.new_graph_property("int")  #定义了一个类型
>>> g.graph_properties["foo"] = gprop   # 定义了一个变量
>>> g.graph_properties["foo"] = 42      # 为变量赋了一个值
>>> print(g.graph_properties["foo"])   #输出变量的值
42
>>> del g.graph_properties["foo"]       # 删除了定义过的变量

如何通过属性访问属性映射？

>>> vprop = g.new_vertex_property("double")
>>> g.vp.foo = vprop    # 等价于g.vertex_properties["foo"] = vprop
>>> v = g.vertex(0)
>>> g.vp.foo[v] = 3.14  #等价于v.vertex_properties["foo"] = 3.14
>>> print(g.vp.foo[v])
3.14

图的I/O

图保存和加载的四种格式？

graphml、dot、gml和gt

图从文件保存和加载的方法，从磁盘加载的方法？

save()

load()

load_graph()

.

g = Graph()
g.save("my_graph.xml.gz")
g2 = load_graph("my_graph.xml.gz")

.

pickle模块

一个例子:构建一个 Price网络

如何看懂Price网络的代码？

#! /usr/bin/env python

# We will need some things from several places
from __future__ import division, absolute_import, print_function
import sys
if sys.version_info < (3,):
range = xrange
import os
from pylab import *  # for plotting
from numpy.random import *  # for random sampling
seed(42)

# We need to import the graph_tool module itself
from graph_tool.all import *

# let's construct a Price network (the one that existed before Barabasi). It is
# a directed network, with preferential attachment. The algorithm below is
# very naive, and a bit slow, but quite simple.

# We start with an empty, directed graph
g = Graph()

# We want also to keep the age information for each vertex and edge. For that
# let's create some property maps
v_age = g.new_vertex_property("int")
e_age = g.new_edge_property("int")

# The final size of the network
N = 100000

# We have to start with one vertex
v = g.add_vertex()
v_age[v] = 0

# we will keep a list of the vertices. The number of times a vertex is in this
# list will give the probability of it being selected.
vlist = [v]

# let's now add the new edges and vertices
for i in range(1, N):
# create our new vertex
v = g.add_vertex()
v_age[v] = i

# we need to sample a new vertex to be the target, based on its in-degree +
# 1. For that, we simply randomly sample it from vlist.
i = randint(0, len(vlist))
target = vlist[i]

# add edge
e = g.add_edge(v, target)
e_age[e] = i

# put v and target in the list
vlist.append(target)
vlist.append(v)

# now we have a graph!

# let's do a random walk on the graph and print the age of the vertices we find,
# just for fun.

v = g.vertex(randint(0, g.num_vertices()))
while True:
print("vertex:", int(v), "in-degree:", v.in_degree(), "out-degree:",
v.out_degree(), "age:", v_age[v])

if v.out_degree() == 0:
print("Nowhere else to go... We found the main hub!")
break

n_list = []
for w in v.out_neighbours():
n_list.append(w)
v = n_list[randint(0, len(n_list))]

# let's save our graph for posterity. We want to save the age properties as
# well... To do this, they must become "internal" properties:

g.vertex_properties["age"] = v_age
g.edge_properties["age"] = e_age

# now we can save it
g.save("price.xml.gz")

# Let's plot its in-degree distribution
in_hist = vertex_hist(g, "in")

y = in_hist[0]
err = sqrt(in_hist[0])
err[err >= y] = y[err >= y] - 1e-2

figure(figsize=(6,4))
errorbar(in_hist[1][:-1], in_hist[0], fmt="o", yerr=err,
label="in")
gca().set_yscale("log")
gca().set_xscale("log")
gca().set_ylim(1e-1, 1e5)
gca().set_xlim(0.8, 1e3)
subplots_adjust(left=0.2, bottom=0.2)
xlabel("$k_{in}$")
ylabel("$NP(k_{in})$")
tight_layout()
savefig("price-deg-dist.pdf")
savefig("price-deg-dist.png")