Mining Top-K Influencers in Social Networks on Heterogeneous Communities

• Main Authors: Hsiang-Pin Wu, 吳祥彬
• Other Authors: Sing-Ling Lee
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/86878388130131857335

碩士 === 國立中正大學 === 資訊工程研究所 === 103 === In this thesis, we discuss how to find top-K influencers in social networks based on the different rates of diffusion between heterogeneous communities. The different rates of diffusion means that when influence spreads to other communities, the influence will be reduced. So our method wants to select persons which have good interpersonal relationships in those communities whose size is larger than the average size. It means intra-community edges and inter-community edges of the selected candidate node both larger than the average of communities. Following the popular persons are selected to be candidates, the candidates have stronger connections in other communities will be selected to be influencers. After selecting influencers, the spread of influence will be simulate by heat diffusion model. In experiments of the different rates of diffusion, two real-world data sets will be used to compare our method with others. The results show that our method has better performance on spread of influence in the large scale of network.