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A multi information dissemination model considering the interference of derivative information

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  • Sun, Ling
  • Liu, Yun
  • Bartolacci, Michael R.
  • Ting, I-Hsien

Abstract

With the tremendous growth of social network research, many information diffusion models have been proposed from multiple perspectives with the intent of finding out key factors. However, most models only focus on the individual behavior patterns or the usage habits of social applications; the potential interrelationships between information items have not been explored. From this point of view, we propose an information interference model that takes into account the interrelationships between information items in social network. The effect of interference and anti-interference abilities of information in diffusion are analyzed in highly clustered regular networks and also the random networks. We find that information diffusion in regular networks is more easily affected by interference information; but the corresponding reduction of the information diffusion range is the negative consequence in random networks. We also find that the individuals who know about information are the main spreaders of interference. From the aspect of the interference, random network shows a higher timeliness requirement to interference. Furthermore, simulation results indicate that increasing initial forwarding probability of information is much better than increasing the influence of it in reducing interference.

Suggested Citation

  • Sun, Ling & Liu, Yun & Bartolacci, Michael R. & Ting, I-Hsien, 2016. "A multi information dissemination model considering the interference of derivative information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 541-548.
    • Handle: RePEc:eee:phsmap:v:451:y:2016:i:c:p:541-548
    DOI: 10.1016/j.physa.2016.01.094
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    Cited by:

    1. Ma, Ning & Liu, Yijun & Chi, Yuxue, 2018. "Influencer discovery algorithm in a multi-relational network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 415-425.

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