An Algorithm Combining Latent Dirichlet Allocation and Bimodal Network for Evaluating Goal Deviation of Intellectual Property Strategy Execution in China

Table 2

Characteristics of provincial nodes.

Province	Degree centrality	Closeness centrality	Betweenness centrality
Beijing	0.231	1.716	0.001
Tianjin	0.231	1.716	0.001
Hebei	0.179	1.62	0.001
Shanxi	0.154	1.575	0
Nei Mongol	0.154	1.575	0.001
Heilongjiang	0.179	1.62	0.001
Jinlin	0.205	1.667	0.001
Liaoning	0.205	1.667	0.001
Shanghai	0.231	1.716	0.001
Jiangsu	0.205	1.667	0.001
Chongqing	0.231	1.716	0.001
Sichuan	0.231	1.716	0.001
Yunnan	0.205	1.667	0.001
Shaanxi	0.179	1.62	0.001
Gansu	0.231	1.716	0.001
Qinghai	0.231	1.716	0.001
Ningxia	0.128	1.533	0
Xinjiang	0.179	1.62	0.001
Hainan	0.103	1.494	0
Guizhou	0.128	1.533	0
Zhejiang	0.205	1.667	0.001
Anhui	0.205	1.667	0.001
Fujian	0.205	1.667	0.001
Jiangxi	0.205	1.667	0.001
Shandong	0.154	1.575	0
Henan	0.205	1.667	0.001
Hubei	0.205	1.667	0.001
Hunan	0.231	1.716	0.001
Guangdong	0.231	1.716	0.001
Guangxi	0.205	1.667	0.001