python教程分享Python利用networkx画图绘制Les Misérables人物关系

数据集介绍

《悲惨世界》中的人物关系图,图中共77个节点、254条边。

数据集截图:

Python利用networkx画图绘制Les Misérables人物关系

打开readme文件:

les misérables network, part of the koblenz network collection  ===========================================================================  this directory contains the tsv and related files of the moreno_lesmis network: this undirected network contains co-occurances of characters in victor hugo's novel 'les misérables'. a node represents a character and an edge between two nodes shows that these two characters appeared in the same chapter of the the book. the weight of each link indicates how often such a co-appearance occured.  more information about the network is provided here:   http://konect.cc/networks/moreno_lesmis  files:       meta.moreno_lesmis -- metadata about the network       out.moreno_lesmis -- the adjacency matrix of the network in whitespace-separated values format, with one edge per line        the meaning of the columns in out.moreno_lesmis are:           first column: id of from node           second column: id of to node          third column (if present): weight or multiplicity of edge          fourth column (if present):  timestamp of edges unix time          third column: edge weight  use the following references for citation:  @misc{konect:2017:moreno_lesmis,      title = {les misérables network dataset -- {konect}},      month = oct,      year = {2017},      url = {http://konect.cc/networks/moreno_lesmis}  }  @book{konect:knuth1993,  	title = {the {stanford} {graphbase}: a platform for combinatorial computing},  	author = {knuth, donald ervin},  	volume = {37},  	year = {1993},  	publisher = {addison-wesley reading},  }  @book{konect:knuth1993,  	title = {the {stanford} {graphbase}: a platform for combinatorial computing},  	author = {knuth, donald ervin},  	volume = {37},  	year = {1993},  	publisher = {addison-wesley reading},  }  @inproceedings{konect,  	title = {{konect} -- {the} {koblenz} {network} {collection}},  	author = {jérôme kunegis},  	year = {2013},  	booktitle = {proc. int. conf. on world wide web companion},  	pages = {1343--1350},  	url = {http://dl.acm.org/citation.cfm?id=2488173},  	url_presentation = {https://www.slideshare.net/kunegis/presentationwow},  	url_web = {http://konect.cc/},  	url_citations = {https://scholar.google.com/scholar?cites=7174338004474749050},  }  @inproceedings{konect,  	title = {{konect} -- {the} {koblenz} {network} {collection}},  	author = {jérôme kunegis},  	year = {2013},  	booktitle = {proc. int. conf. on world wide web companion},  	pages = {1343--1350},  	url = {http://dl.acm.org/citation.cfm?id=2488173},  	url_presentation = {https://www.slideshare.net/kunegis/presentationwow},  	url_web = {http://konect.cc/},  	url_citations = {https://scholar.google.com/scholar?cites=7174338004474749050},  }  

从中可以得知:该图是一个无向图,节点表示《悲惨世界》中的人物,两个节点之间的边表示这两个人物出现在书的同一章,边的权重表示两个人物(节点)出现在同一章中的频率。

真正的数据在out.moreno_lesmis_lesmis中,打开并另存为csv文件:

Python利用networkx画图绘制Les Misérables人物关系

数据处理

networkx中对无向图的初始化代码为:

g = nx.graph()  g.add_nodes_from([i for i in range(1, 78)])  g.add_edges_from([(1, 2, {'weight': 1})])  

节点的初始化很容易解决,我们主要解决边的初始化:先将dataframe转为列表,然后将其中每个元素转为元组。

df = pd.read_csv('out.csv')  res = df.values.tolist()  for i in range(len(res)):      res[i][2] = dict({'weight': res[i][2]})  res = [tuple(x) for x in res]  print(res)  

res输出如下(部分):

[(1, 2, {'weight': 1}), (2, 3, {'weight': 8}), (2, 4, {'weight': 10}), (2, 5, {'weight': 1}), (2, 6, {'weight': 1}), (2, 7, {'weight': 1}), (2, 8, {'weight': 1})...]  

因此图的初始化代码为:

g = nx.graph()  g.add_nodes_from([i for i in range(1, 78)])  g.add_edges_from(res)  

画图

nx.draw(g)  plt.show()  

Python利用networkx画图绘制Les Misérables人物关系

networkx自带的数据集

忙活了半天发现networkx有自带的数据集,其中就有悲惨世界的人物关系图:

g = nx.les_miserables_graph()  nx.draw(g, with_labels=true)  plt.show()  

Python利用networkx画图绘制Les Misérables人物关系

完整代码

# -*- coding: utf-8 -*-  import networkx as nx  import matplotlib.pyplot as plt  import pandas as pd  # 77 254  df = pd.read_csv('out.csv')  res = df.values.tolist()  for i in range(len(res)):      res[i][2] = dict({'weight': res[i][2]})  res = [tuple(x) for x in res]  print(res)  # 初始化图  g = nx.graph()  g.add_nodes_from([i for i in range(1, 78)])  g.add_edges_from(res)  g = nx.les_miserables_graph()  nx.draw(g, with_labels=true)  plt.show()  

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