graph-tool文档（一）- 快速开始使用Graph-tool - 2.属性映射、图的IO和Price网络

目录：

• 属性映射
  
  -- 内部属性映射
• 图的I/O
• 构建一个 Price网络（例）

名词解释：

Property maps：属性映射

PropertyMap：一个类

scalar value types：标量值类型

pickle module：

scale-free graph：

属性映射

属性映射是一种将额外信息与顶点、边或图本身相关联的方式。

因此有这样三种类型的属性映射:顶点、边和图。

它们都是由同一个类来操作：PropertyMap。

每个创建了的属性映射都有一个与之相关联的类型的值，预定义设置的类型有如下几种：

Type name	Alias
bool	uint8_t
int16_t	short
int32_t	int
int64_t	long, long long
double	float
long double	.
string	.
vector bool	vector uint8_t
vector int16_t	vector short
vector int32_t	vector int
vector int64_t	vector long, vector long long
vector double	vector float
vector long double	.
vector string	.
python::object	object

可以对于每一个映射类型通过调用new_vertex_property()，new_edge_property()或new_graph_property()为一个指定的图创建新的属性映射。

然后可以通过顶点或边的描述符或图本身来访问该值,因此:

from itertools import izip
from numpy.random import randint

g = Graph()
g.add_vertex(100)
# insert some random links
for s,t in izip(randint(0, 100, 100), randint(0, 100, 100)):
    g.add_edge(g.vertex(s), g.vertex(t))

vprop_double = g.new_vertex_property("double")            # Double-precision floating point
vprop_double[g.vertex(10)] = 3.1416

vprop_vint = g.new_vertex_property("vector<int>")         # Vector of ints
vprop_vint[g.vertex(40)] = [1, 3, 42, 54]

eprop_dict = g.new_edge_property("object")                # Arbitrary python object.
eprop_dict[g.edges().next()] = {"foo": "bar", "gnu": 42}  # In this case, a dict.

gprop_bool = g.new_graph_property("bool")                  # Boolean
gprop_bool[g] = True

标量值类型的属性映射也可以被当做numpy.ndarray来访问，通过get_array()方法，或者a属性。

from numpy.random import random

# this assigns random values to the vertex properties
vprop_double.get_array()[:] = random(g.num_vertices())

# or more conveniently (this is equivalent to the above)
vprop_double.a = random(g.num_vertices())

内部属性映射

任何创建的属性映射可以作为“内部”到相应的图上。

这意味着它将被复制并和图一起被保存到一个文件。

属性被内在化，通过将它们包括在图的类字典属性中：vertex_properties，edge_properties或graph_properties(或它们的别名,vp,ep或gp)。

当插入到图中时,属性映射必须有一个唯一的名称(相同类型的之间):

>>> eprop = g.new_edge_property("string")
>>> g.edge_properties["some name"] = eprop
>>> g.list_properties()
some name      (edge)    (type: string)

内部图的属性映射表现得略有不同。

它不是返回属性映射对象，值本身是从字典中返回的:

>>> gprop = g.new_graph_property("int")
>>> g.graph_properties["foo"] = gprop   # this sets the actual property map
>>> g.graph_properties["foo"] = 42      # this sets its value
>>> print(g.graph_properties["foo"])
42
>>> del g.graph_properties["foo"]       # the property map entry is deleted from the dictionary

为了方便起见,内部属性映射也可以通过属性来访问:

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

图的I/O

图可以通过四种格式保存和加载：graphml、dot、gml和一个定制的二进制格式gt(见gt文件格式)。

警告：

二进制格式gt和graphml是首选的格式，因为它们是迄今为止最完整的。

这些格式都是同样完整的,但gt速度更快,需要的存储空间也更少。

图可以保存或加载到一个文件上，通过save和load方法，以一个文件名或类似文件的对象。

图也可以从光盘上加载，通过load_graph()函数，如下:

g = Graph()
#  ... fill the graph ...
g.save("my_graph.xml.gz")
g2 = load_graph("my_graph.xml.gz")
# g and g2 should be copies of each other

图类也可以通过pickle模块来pickled with。

一个例子:构建一个 Price网络

Price网络是第一个已知的“无尺度”图模型，于1976年被de Solla Price发明。

它是被动态定义的，每一步添加一个新的顶点到图中，并连接到一个旧的顶点，概率与它的入度成正比。

下面的程序使用graph-tool实现了这个结构。

注意：

只使用price_network()函数将会快得多,因为它是以c++实现的，而不是像下面的脚本一样使用纯python。

下面的代码仅仅是一个如何使用该库的示例。

#! /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")

下面是程序的运行结果：

vertex: 36063 in-degree: 0 out-degree: 1 age: 36063
vertex: 9075 in-degree: 4 out-degree: 1 age: 9075
vertex: 5967 in-degree: 3 out-degree: 1 age: 5967
vertex: 1113 in-degree: 7 out-degree: 1 age: 1113
vertex: 25 in-degree: 84 out-degree: 1 age: 25
vertex: 10 in-degree: 541 out-degree: 1 age: 10
vertex: 5 in-degree: 140 out-degree: 1 age: 5
vertex: 2 in-degree: 459 out-degree: 1 age: 2
vertex: 1 in-degree: 520 out-degree: 1 age: 1
vertex: 0 in-degree: 210 out-degree: 0 age: 0
Nowhere else to go... We found the main hub!

下面是100000个节点的度的分布。

如果你想看到一个更广泛的幂律，可以尝试增加顶点的数量到(10 ^ 6)或(10 ^ 7)。



(10 ^ 5)个节点的Price网络的入度分布。

我们可以画图来观察它的一些其他的拓扑特性。

为此，我们可以使用graph_draw()函数。

g = load_graph("price.xml.gz")
age = g.vertex_properties["age"]

pos = sfdp_layout(g)
graph_draw(g, pos, output_size=(1000, 1000), vertex_color=[1,1,1,0],
           vertex_fill_color=age, vertex_size=1, edge_pen_width=1.2,
           vcmap=matplotlib.cm.gist_heat_r, output="price.png")

一个有(10 ^ 5 )个节点的Price网络。

顶点颜色代表顶点的年龄，旧的(红色)，新的(黑)。

原文链接：Quick start using graph-tool