Python利用networkx畫圖繪制Les?Misérables人物關(guān)系

更新時間：2022年05月11日 16:01:30 作者：Cyril_KI

這篇文章主要為大家介紹了Python利用networkx畫圖處理繪制Les?Misérables悲慘世界里的人物關(guān)系圖，有需要的朋友可以借鑒參考下，希望能夠有所幫助，祝大家多多進步，早日升職加薪

數(shù)據(jù)集介紹

《悲慘世界》中的人物關(guān)系圖，圖中共77個節(jié)點、254條邊。

數(shù)據(jù)集截圖：

打開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 = {Jér?me 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 = {Jér?me 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},
}

從中可以得知：該圖是一個無向圖，節(jié)點表示《悲慘世界》中的人物，兩個節(jié)點之間的邊表示這兩個人物出現(xiàn)在書的同一章，邊的權(quán)重表示兩個人物（節(jié)點）出現(xiàn)在同一章中的頻率。

真正的數(shù)據(jù)在out.moreno_lesmis_lesmis中，打開并另存為csv文件：

數(shù)據(jù)處理

networkx中對無向圖的初始化代碼為：

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

節(jié)點的初始化很容易解決，我們主要解決邊的初始化：先將dataframe轉(zhuǎn)為列表，然后將其中每個元素轉(zhuǎn)為元組。

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輸出如下（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})...]

因此圖的初始化代碼為：

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

畫圖

nx.draw(g)
plt.show()

networkx自帶的數(shù)據(jù)集

忙活了半天發(fā)現(xiàn)networkx有自帶的數(shù)據(jù)集，其中就有悲慘世界的人物關(guān)系圖：

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

完整代碼

# -*- 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)
# 初始化圖
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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