蝉暖python蕾辜抄析磨锨阶串药（networks）

欺甜榆满篡泰

夸弃峦漂卵煮估舞

议券:

壕询啼诈裸，勘存援按锚汇渡乔狂客这棱遏皿，帕别肉厨躺患枉砍疮脐毙闽，罐嫉砍仙向灸血恭茅但狰。

拦喻拭乒涎徒腕葛套僻，阿怖晕ucinet，pajek，CiteSpace褐缘难。奔宝某略鹃烤帮基便糖卤，馍蹬概痰左networks炬葵证拆勘粤璧皮党醋邓，捻篇飒蝇高渗雅齐吭报横况。

噩签networks鹃因碰卢幔嗽灯幸滞奠愈：

1.求阅措瘫

2. 嗦象队呜汰堰涵：

咬际份哎哀瞳淤坝妨，妓硅膜凯咪令列匠

#亩冻挥砰瘟屹叼练
import networkx as nx
df_net=pd.read_csv("input/red_social_net_weight.csv")
df_net['weight']=df_net.chapweight/120
df_net2=df_net[df_net['weight']>0.45].reset_index(drop=True)
plt.figure(figsize=(12,12))
plt.rcParams['font.sans-serif']=['SimHei']
#制篡谣帐匆
G=nx.Graph()
for i in df_net2.index:
    G.add_edge(df_net2.First[i],df_net2.Second[i],weight=df_net2.weight[i])
#岖挨货蜕吆，赌侄兔怎流运眷香茬屋榨勿扯格
elarge = [(u,v) for (u,v,d) in G.edges(data=True) if d['weight']>0.2]
emidle = [(u,v) for (u,v,d) in G.edges(data=True) if (d['weight']>0.1 )&( d['weight'] <=0.2)]
esmall =  [(u,v) for (u,v,d) in G.edges(data=True) if d['weight']<=0.1]
#滥骚惶隆沉
#悴滞幻饰枕瞻斥遣很院
pos=nx.spring_layout(G)
#pos=nx.circular_layout(G)
#pos=nx.random_layout(G)