Journal article

Optimizing online social networks for information propagation

  • Chen, Duan-Bing Department of Physics, University of Fribourg, Switzerland - Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
  • Wang, Guan-Nan Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
  • Zeng, An Department of Physics, University of Fribourg, Switzerland
  • Fu, Yan Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
  • Zhang, Yi-Cheng Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China - Department of Physics, University of Fribourg, Switzerland
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    09.05.2014
Published in:
  • PLoS ONE. - 2014, vol. 9, no. 5, p. e96614
English Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their leaders. Validation of such adaptive social recommendation methods in the literature assumes uniform distribution of users' activity frequency. In this paper, our empirical analysis shows that the distribution of online users' activity is actually heterogenous. Accordingly, we propose a more realistic multi-agent model in which users' activity frequency are drawn from a power-law distribution. We find that previous social recommendation methods lead to serious delay of information propagation since many users are connected to inactive leaders. To solve this problem, we design a new similarity measure which takes into account users' activity frequencies. With this similarity measure, the average delay is significantly shortened and the recommendation accuracy is largely improved.
Faculty
Faculté des sciences
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/303818
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