Open Access
ARTICLE
Identifying Event-Specific Opinion Leaders by Local Weighted LeaderRank
Wanxia Yang1,*, Sadaqatur Rehman2, Wenhui Que3
1 Mechanical and Electrical Engineering college, Gansu Agricultural University, Lanzhou, 730070, China
2 Department of Computer Science, Namal Institute, Mianwali, 42250, Pakistan
3 Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing, China
* Corresponding Author: Wanxia Yang. Email:
Intelligent Automation & Soft Computing 2020, 26(6), 1561-1574. https://doi.org/10.32604/iasc.2020.012480
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.
Keywords
Cite This Article
W. Yang, S. Rehman and W. Que, "Identifying event-specific opinion leaders by local weighted leaderrank,"
Intelligent Automation & Soft Computing, vol. 26, no.6, pp. 1561–1574, 2020. https://doi.org/10.32604/iasc.2020.012480
Citations