- 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_propertiesvp
edge_propertiesep
graph_propertiesgp -
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")