2018-07-04 10:07:36 Farmer001

知識圖譜如火如荼，首先推薦在python下進行社交網絡分析networkx

建立圖網絡

無向圖

import networkx as nxG = nx.Graph() #建立一個空的無向圖GG.add_node(1) #添加一個節點1G.add_edge(2,3) #添加一條邊2-3（隱含著添加了兩個節點2、3）G.add_edge(3,2) #對於無向圖，邊3-2與邊2-3被認為是一條邊print (G.nodes()) #輸出全部的節點： [1, 2, 3]print (G.edges()) #輸出全部的邊：[(2, 3)]print (G.number_of_edges()) #輸出邊的數量：1

[1, 2, 3][(2, 3)]1

有向圖

將G = nx.Graph() 改為 G = nx.DiGraph()即進行有向圖，表示不同的邊

import networkx as nxG = nx.DiGraph() #建立一個空的無向圖GG.add_node(1) #添加一個節點1G.add_edge(2,3) #添加一條邊2-3（隱含著添加了兩個節點2、3）G.add_edge(3,2) #對於無向圖，邊3-2與邊2-3被認為是一條邊print (G.nodes()) #輸出全部的節點： [1, 2, 3]print (G.edges()) #輸出全部的邊：[(2, 3), (3, 2)]print (G.number_of_edges()) #輸出邊的數量：

[1, 2, 3][(2, 3), (3, 2)]2

同時，有向圖和無向圖是可以相互轉化的，分別用到Graph.to_undirected() 和 Graph.to_directed()兩個方法。

帶權圖

有向圖和無向圖都可以給邊賦予權重，用到的方法是add_weighted_edges_from，它接受1個或多個三元組[u,v,w]作為參數，其中u是起點，v是終點，w是權重。例如：

G.add_weighted_edges_from([(0,1,3.0),(1,2,7.5)])

添加0-1和1-2兩條邊，權重分別是3.0和7.5。

如果想讀取權重，可以使用get_edge_data方法，它接受兩個參數u和v，即邊的起訖點。例如：

import networkx as nx

G = nx.DiGraph()

G.add_node(1)

G.add_edge(2,3)

G.add_edge(3,2)

G.add_weighted_edges_from([(0,1,3.0),(1,2,7.5)])

print (G.nodes())

print (G.edges())

print (G.number_of_edges())

print (G.get_edge_data(1,2))

[1, 2, 3, 0]

[(1, 2), (2, 3), (3, 2), (0, 1)]

{‘weight’: 7.5}

NetworkX提供了常用的圖論經典算法，例如DFS、BFS、最短路、最小生成樹、最大流等等

#調用多源最短路徑算法，計算圖G所有節點間的最短路徑

path=dict(nx.all_pairs_shortest_path(G))print (path[0][2])

[0, 1, 2]

一個完整官方小案例

 import networkx as nx G = nx.Graph()G.add_edge('A', 'B', weight=4) G.add_edge('B', 'D', weight=2) G.add_edge('A', 'C', weight=3) G.add_edge('C', 'D', weight=4)nx.shortest_path(G, 'A', 'D', weight='weight')

[‘A’, ‘B’, ‘D’]

圖數據的保存與繪圖

import networkx as nximport matplotlib.pyplot as pltG = nx.DiGraph()G.add_node(1)G.add_edge(2,3)G.add_edge(3,2)nx.write_edgelist(G, path="grid.edgelist", delimiter=":")# write edgelist to grid.edgelistnx.write_edgelist(G, path="grid.edgelist", delimiter=":")# read edgelist from grid.edgelistH = nx.read_edgelist(path="grid.edgelist", delimiter=":")

nx.draw(H)plt.show()

數據庫基本統計

!usr/bin/env python

* coding:utf-8 *

import matplotlib.pyplot as plt

from networkx import nx

G = nx.lollipop_graph(4, 6)

pathlengths = []

print(“source vertex {target:length, }”)

for v in G.nodes():

spl = dict(nx.single_source_shortest_path_length(G, v))

print(‘{} {} ‘.format(v, spl))

for p in spl:

pathlengths.append(spl[p])

print(”)

print(“average shortest path length %s” % (sum(pathlengths) / len(pathlengths)))

histogram of path lengths

dist = {}for p in pathlengths: if p in dist: dist[p] += 1 else: dist[p] = 1print('')print("length #paths")verts = dist.keys()for d in sorted(verts): print('%s %d' % (d, dist[d]))print("radius: %d" % nx.radius(G))print("diameter: %d" % nx.diameter(G))print("eccentricity: %s" % nx.eccentricity(G))print("center: %s" % nx.center(G))print("periphery: %s" % nx.periphery(G))print("density: %s" % nx.density(G))