@Article{iasc.2020.012480,
AUTHOR = {Wanxia Yang, Sadaqatur Rehman, Wenhui Que},
TITLE = {Identifying Event-Specific Opinion Leaders by Local Weighted LeaderRank},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {6},
PAGES = {1561--1574},
URL = {http://www.techscience.com/iasc/v26n6/40834},
ISSN = {2326-005X},
ABSTRACT = {Identifying event-specific opinion leaders is essential for understanding
event developments and influencing public opinion. News articles are informative 
and formal in expression, and include valuable information on specific events. In 
this paper, we propose an improved variant of LeaderRank, called local weighted 
LeaderRank, to measure the event-specific influence of person nodes in a weighted 
and undirected person cooccurrence network constructed using news articles 
related to a specific event. Our proposed method measures the influence of person 
nodes by considering both the cooccurrence strength between persons, and 
additional local link weight information for each local person node. To evaluate the 
performance of our method, we use the weighted susceptible infected (WSI) model 
to simulate the influence-spreading process in real-person cooccurrence networks. 
The experiment results obtained after measuring the rank correlations between the 
rank list generated by the simulation results and those generated by the influence 
measures show that our method identifies event-specific opinion leaders effectively 
and performs better than other state-of-the-art influence measures, such as 
weighted K-shell decomposition and the weighted local centrality.},
DOI = {10.32604/iasc.2020.012480}
}