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Title: Influence Analysis for Online Social Networks
Keywords: social influence analysis, influential path, topic-level influence, consistent influencer, social networks, network data
Issue Date: 14-Jul-2014
Source: XU ENLIANG (2014-07-14). Influence Analysis for Online Social Networks. ScholarBank@NUS Repository.
Abstract: The prevalence of online social media such as Facebook, Twitter, LinkedIn and YouTube has attracted considerable research in social influence analysis with applications in viral marketing, online advertising, recommender systems, information diffusion, and experts finding. Social influence occurs when one?s emotions, opinions, or behaviors are affected by others. In this thesis, we perform influence analysis for online social networks by addressing three important issues in the discovery of influential nodes and influence relationships, which have been given little attention by existing works: influential path, topic-level influence and consistent influencer. We show that exploiting these issues can further benefit social influence analysis. Specifically, we propose algorithms to discover influential paths, infer topic-level influence and identify k-consistent influencers in social network data, and conduct experiments on real world datasets to demonstrate the important applications of proposed methods, such as user behavior prediction, influence maximization and experts finding.
Appears in Collections:Ph.D Theses (Open)

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