@inproceedings{995bb07802aa48e49c8763830f2e5cee,
title = "Mining opinion leaders in big social network",
abstract = "Recently, due to the popularity of Web 2.0, considerable attention has been paid to the opinion leader discovery in social network. By identifying the opinion leaders, companies or governments can manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, mining opinion leaders in a huge social network is a challenge task because of the complexity of graph processing and leadership analysis. In this study, a novel algorithm, OLMiner, is proposed to efficiently find the opinion leaders from a huge social network. We propose a clustering method to solve the influence overlapping issue and significantly reduce the computation time by shrinking the size of candidate generation. The experimental results show that the proposed OLMiner can effectively discover the influential opinion leaders in different real social networks with efficiency.",
keywords = "Clustering, Opinion leader, Semantic analysis, Social network",
author = "Chen, {Yi Cheng} and Chen, {Yi Hsiang} and Hsu, {Chia Hao} and You, {Hao Jun} and Jianquan Liu and Xin Huang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017 ; Conference date: 27-03-2017 Through 29-03-2017",
year = "2017",
month = may,
day = "5",
doi = "10.1109/AINA.2017.147",
language = "???core.languages.en_GB???",
series = "Proceedings - International Conference on Advanced Information Networking and Applications, AINA",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1012--1018",
editor = "Tomoya Enokido and Hui-Huang Hsu and Chi-Yi Lin and Makoto Takizawa and Leonard Barolli",
booktitle = "Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017",
}