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Title: Persuasion driven influence analysis in online social networks
Authors: Yi, X
Shen, X
Lu, W
Chan, TS
Chung, FL 
Keywords: Affinity propagation
Graph clustering
Social influence analysis
Social persuasion
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings of the International Joint Conference on Neural Networks, 2016, v. 2016-October, 7727782, p. 4451-4456 How to cite?
Abstract: It is now a fact as well as a trend that people are increasingly relying on online social networks to work, study, and share with others. Thus, it is unavoidable for us to be influenced by others through online social networking. Studying social influence and information diffusion in online social networks can be remarkably useful in various real-life applications, notably influencer marketing and viral marketing. The Topical Affinity Propagation (TAP) model has been demonstrated with success to analyze the social influence on topic level. It manages to identify the most influential nodes on a given topic in social networks successfully. While TAP mainly focuses on the topic factor, it considers little about the complex relationship between individuals, which is crucial in calculating the influence probabilities between nodes. In this paper, the idea of quantitatively estimating the peer influence probability from a social persuasion perspective in sociology is exploited and consequently a persuasion-driven social influence analysis model is presented. Based on the TAP model and the social persuasion influence propagation measures, both the topical information and the persuasion influences between individuals are taken into consideration such that a social persuasion-driven influence analysis model is proposed. The experimental results on different data sets show that the social influences on a given topic can be discovered effectively by the proposed approach, especially under consideration of authority, the accuracy in identifying the most influential nodes can be significantly improved as compared with an existing work.
Description: 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, Canada, 24-29 July 2016
ISBN: 9781509006199
DOI: 10.1109/IJCNN.2016.7727782
Appears in Collections:Conference Paper

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