Robust learning in social networks via matrix scaling

Yakov Babichenko, Segev Shlomov

Research output: Contribution to journalArticlepeer-review


The influence vanishing property in social networks states that the influence of the most influential agent vanishes as society grows. Removing this assumption causes a failure of learning of boundedly rational dynamics. We suggest a boundedly rational methodology that leads to learning in almost all networks. The methodology adjusts the agent's weights based on the Sinkhorn-Knopp matrix scaling algorithm. It is a simple, local, Markovian, and time-independent methodology that can be applied to multiple settings.

Original languageEnglish
Pages (from-to)720-727
Number of pages8
JournalOperations Research Letters
Issue number5
StatePublished - Sep 2021


  • Information aggregation
  • Learning in networks
  • Non-Bayesian learning
  • Social networks

All Science Journal Classification (ASJC) codes

  • Software
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics


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