標題: Identify influential social network spreaders
作者: Huang, Chung-Yuan
Fu, Yu-Hsiang
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: 2014
摘要: Identifying the most influential individuals spreading ideas, information, or infectious diseases is a topic receiving significant attention from network researchers, since such identification can assist or hinder information dissemination, product exposure, or contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, few efforts have been made to use node diversity within network structures to measure spreading ability. The two-step framework described in this paper uses a robust and reliable measure that combines global diversity and local features to identify the most influential network nodes. Results from a series of SusceptibleInfected- Recovered (SIR) epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets.
URI: http://dx.doi.org/10.1109/ICDMW.2014.31
http://hdl.handle.net/11536/136493
ISBN: 978-1-4799-4274-9
DOI: 10.1109/ICDMW.2014.31
期刊: 2014 IEEE International Conference on Data Mining Workshop (ICDMW)
起始頁: 562
結束頁: 568
顯示於類別:會議論文