Robust learning in social networks via matrix scaling

Yakov Babichenko, Segev Shlomov

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء

ملخص

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.

اللغة الأصليةالإنجليزيّة
الصفحات (من إلى)720-727
عدد الصفحات8
دوريةOperations Research Letters
مستوى الصوت49
رقم الإصدار5
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - سبتمبر 2021

All Science Journal Classification (ASJC) codes

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

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