Python:如何将列表列表的元素转换为无向图？

Question

我有一个程序可以检索 PubMed 出版物的列表，并希望构建一个共同作者图，这意味着对于每篇文章，我想将每个作者(如果不存在)添加为一个顶点并添加一个无向边(或增加其权重)在每位合著者之间。

我设法编写了程序的第一个检索每个出版物的作者列表的程序，并且我知道我可以使用 NetworkX 库来构建图形(然后将其导出到 Gephi 的 GraphML)，但无法全神贯注于如何将“列表列表”转换为图形。

下面是我的代码。非常感谢。

### if needed install the required modules
### python3 -m pip install biopython
### python3 -m pip install numpy

from Bio import Entrez
from Bio import Medline
Entrez.email = "rja@it.com"
handle = Entrez.esearch(db="pubmed", term='("lung diseases, interstitial"[MeSH Terms] NOT "pneumoconiosis"[MeSH Terms]) AND "artificial intelligence"[MeSH Terms] AND "humans"[MeSH Terms]', retmax="1000", sort="relevance", retmode="xml")
records = Entrez.read(handle)
ids = records['IdList']
h = Entrez.efetch(db='pubmed', id=ids, rettype='medline', retmode='text')
#now h holds all of the articles and their sections
records = Medline.parse(h)
# initialize an empty vector for the authors
authors = []
# iterate through all articles
for record in records:
    #for each article (record) get the authors list
    au = record.get('AU', '?')
    # now from the author list iterate through each author
    for a in au: 
        if a not in authors:
            authors.append(a)
    # following is just to show the alphabetic list of all non repeating 
    # authors sorted alphabetically (there should become my graph nodes)
    authors.sort()
    print('Authors: {0}'.format(', '.join(authors)))

Answer 1

太棒了——代码在运行，所以数据结构很清晰!作为一种方法，我们为文章/作者和作者/共同作者构建连接矩阵。

作者名单:如果要描述文章和作者之间的关系，我想你需要每篇文章的作者列表

authors = []
author_lists = []              # <--- new
for record in records:
    au = record.get('AU', '?')
    author_lists.append(au)    # <--- new
    for a in au: 
        if a not in authors: authors.append(a)
authors.sort()
print(authors)

numpy、pandas matplotlib - 这正是我习惯的工作方式

import numpy as np
import pandas as pd
import matplotlib.pylab as plt

AU = np.array(authors)        # authors as np-array
NA = AU.shape[0]              # number of authors

NL = len(author_lists)        # number of articles/author lists
AUL = np.array(author_lists)  # author lists as np-array

print('NA, NL', NA,NL)

连接文章/作者

CON = np.zeros((NL,NA),dtype=int) # initializes connectivity matrix
for j in range(NL):               # run through the article's author list 
    aul = np.array(AUL[j])        # get a single author list as np-array
    z = np.zeros((NA),dtype=int)
    for k in range(len(aul)):     # get a singel author
        z += (AU==aul[k])         # get it's position in the AU, add it  up
    CON[j,:] = z                  # insert the result in the connectivity matrix

#---- grafics --------
fig = plt.figure(figsize=(20,10)) ; 
plt.spy(CON, marker ='s', color='chartreuse', markersize=5)
plt.xlabel('Authors'); plt.ylabel('Articles'); plt.title('Authors of the articles', fontweight='bold')
plt.show()

Connectivity authors/co-authors，生成的矩阵是对称的

df = pd.DataFrame(CON)          # let's use pandas for the following step
ACON = np.zeros((NA,NA))         # initialize the conncetivity matrix
for j in range(NA):              # run through the authors
    df_a = df[df.iloc[:, j] >0]  # give all rows with author j involved
    w = np.array(df_a.sum())     # sum the rows, store it in np-array 
    ACON[j] = w                  # insert it in the connectivity matrix

#---- grafics --------
fig = plt.figure(figsize=(10,10)) ; 
plt.spy(ACON, marker ='s', color='chartreuse', markersize=3)
plt.xlabel('Authors'); plt.ylabel('Authors'); plt.title('Authors that are co-authors', fontweight='bold')
plt.show()

对于带有Networkx 的图形，我认为您需要清楚地知道您想要表示什么，因为有很多点和很多可能性(也许您发布了一个示例？)。下面仅绘制了一些作者圈子。

import networkx as nx

def set_edges(Q):
    case = 'A'
    if case=='A':
        Q1 = np.roll(Q,shift=1)
        Edges = np.vstack((Q,Q1)).T
    return Edges

Q = nx.Graph()
Q.clear()

AT = np.triu(ACON)                        # only the tridiagonal is needed
fig = plt.figure(figsize=(7,7)) ;
for k in range (9):
    iA = np.argwhere(AT[k]>0).ravel()     # get the indices with AT{k}>0
    Edges = set_edges(iA)                 # select the involved nodes and set the edges
    Q.add_edges_from(Edges, with_labels=True)
nx.draw(Q, alpha=0.5)
plt.title('Co-author-ship', fontweight='bold')
plt.show()