Task5 作者信息关联

Task5：作者信息关联

data  = [] #初始化
#使用with语句优势：1.自动关闭文件句柄；2.自动显示（处理）文件读取数据异常
with open("/home/aistudio/data/data67990/arxiv-metadata-oai-2019.json", 'r') as f: 
    for idx, line in enumerate(f): 
        d = json.loads(line)
        d = {
    
    'authors_parsed': d['authors_parsed']}
        data.append(d)
        
data = pd.DataFrame(data) #将list变为dataframe格式，方便使用pandas进行分析

import networkx as nx 
# 创建无向图
G = nx.Graph()

# 只用五篇论文进行构建
for row in data.iloc[:5].itertuples():
    authors = row[1]
    authors = [' '.join(x[:-1]) for x in authors]
    
    # 第一个作者 与 其他作者链接
    for author in authors[1:]:
        G.add_edge(authors[0],author) #　添加节点２，３并链接２３节点

        nx.draw(G, with_labels=True)

try:
    print(nx.dijkstra_path(G, 'Balázs C.', 'Ziambaras Eleni'))
except:
    print('No path')

No path

# 计算论文关系中有多少个联通子图
print(len(nx.communicability(G)))
# 计算论文关系中有多少个联通子图
degree_sequence = sorted([d for n, d in G.degree()], reverse=True)
dmax = max(degree_sequence)

plt.loglog(degree_sequence, "b-", marker="o")
plt.title("Degree rank plot")
plt.ylabel("degree")
plt.xlabel("rank")

# draw graph in inset
plt.axes([0.45, 0.45, 0.45, 0.45])
Gcc = G.subgraph(sorted(nx.connected_components(G), key=len, reverse=True)[0])

pos = nx.spring_layout(Gcc)
plt.axis("off")
nx.draw_networkx_nodes(Gcc, pos, node_size=20)
nx.draw_networkx_edges(Gcc, pos, alpha=0.4)
plt.show()

13