標題: Using global diversity and local features to identify influential social network spreaders
作者: Fu, Yu-Hsiang
Huang, Chung-Yuan
Sun, Chuen-Tsai
資訊工程學系
Department of Computer Science
關鍵字: network diversity;entropy;social network analysis;k-shell decomposition;epidemic model
公開日期: 1-Jan-2014
摘要: The identification of influential spreaders of information via social networks can assist in the acceleration or hindrance of information dissemination, in increased product exposure, and in the detection of contagious disease outbreaks. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, researchers have overlooked node diversity within network structures as a means of measuring spreading ability. The two-step framework described in this paper uses a robust and insensitive measure that combines global diversity and local features (e.g., degree centrality) to identify the most influential social network nodes. Preliminary experiment results indicate that the proposed method performs well and maintains stability in single initial spreader scenarios associated with different social network datasets.
URI: http://hdl.handle.net/11536/129809
ISBN: 978-1-4799-5877-1
ISSN: 
期刊: 2014 PROCEEDINGS OF THE IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2014)
起始頁: 948
結束頁: 953
Appears in Collections:Conferences Paper