Link recommendation for promoting information diffusion in social networks

Authors:
Dong Li;Zhiming Xu;Sheng Li;Xin Sun;Anika Gupta;Katia Sycara
Affiliations:
Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA
Venue:
Proceedings of the 22nd international conference on World Wide Web companion
Year:
2013

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Abstract

Online social networks mainly have two functions: social interaction and information diffusion. Most of current link recommendation researches only focus on strengthening the social interaction function, but ignore the problem of how to enhance the information diffusion function. For solving this problem, this paper introduces the concept of user diffusion degree and proposes the algorithm for calculating it, then combines it with traditional recommendation methods for reranking recommended links. Experimental results on Email dataset and Amazon dataset under Independent Cascade Model and Linear Threshold Model show that our method noticeably outperforms the traditional methods in terms of promoting information diffusion.