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A Structural Model for Detecting Communities in Networks

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  • Alex Centeno

Abstract

The objective of this paper is to identify and analyze the response actions of a set of players embedded in sub-networks in the context of interaction and learning. We characterize strategic network formation as a static game of interactions where players maximize their utility depending on the connections they establish and multiple interdependent actions that permit group-specific parameters of players. It is challenging to apply this type of model to real-life scenarios for two reasons: The computation of the Bayesian Nash Equilibrium is highly demanding and the identification of social influence requires the use of excluded variables that are oftentimes unavailable. Based on the theoretical proposal, we propose a set of simulant equations and discuss the identification of the social interaction effect employing multi-modal network autoregressive.

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  • Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.
    • Handle: RePEc:arx:papers:2209.08380
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