Robust learning in social networks via matrix scaling

Yakov Babichenko; Segev Shlomov

doi:https://doi.org/10.1016/j.orl.2021.08.002

Robust learning in social networks via matrix scaling

Yakov Babichenko, Segev Shlomov

Data and Decision Sciences

Technion - Israel Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

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 language	English
Pages (from-to)	720-727
Number of pages	8
Journal	Operations Research Letters
Volume	49
Issue number	5
DOIs	https://doi.org/10.1016/j.orl.2021.08.002
State	Published - Sep 2021

Keywords

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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https://doi.org/10.1016/j.orl.2021.08.002

Cite this

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title = "Robust learning in social networks via matrix scaling",

abstract = "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.",

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year = "2021",

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N2 - 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.

AB - 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.

KW - Information aggregation

KW - Learning in networks

KW - Non-Bayesian learning

KW - Social networks

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DO - https://doi.org/10.1016/j.orl.2021.08.002

M3 - مقالة

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SP - 720

EP - 727

JO - Operations Research Letters

JF - Operations Research Letters

IS - 5

ER -

Robust learning in social networks via matrix scaling

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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