Modeling Information Dissemination Dynamics and Referral Selection in Online Social Networks

• Main Authors: Hsin-Heng Haung, 黃信珩
• Other Authors: Ray-Guang Cheng
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/82702641446526779907

碩士 === 國立臺灣科技大學 === 電子工程系 === 100 === Online social networks (OSNs) are among the most popular sites and communication tools which allow human interact with each other and disseminate information over the Internet. A generic and reliable model is required to capture the information dissemination dynamics of interactions in social networks. Inspired from epidemiology, we present an analytical model to capture the information dissemination dynamics in OSNs. Validated by simulations, the proposed model serves successfully approximating the knowledge of information dissemination dynamics in OSNs. A viral-marketing-based approach was proposed to identify the most influential users (referrals) to disseminate information in OSNs. In our works, we present a game-theoretic framework to model user behavior of disseminating information due to the effect of social dynamics. Consider the effect of social dynamics and social interaction using OSNs, a referral selection algorithm is presented to maximize the popularity of information. Validated the simulations, the proposed algorithm can achieve the better performances in different application scenarios.