Distributed Learning from Interactions in Social Networks

TL;DR

This paper introduces a Bayesian framework for agents in social networks to learn their states through distributed algorithms based on scores and interactions, combining local classifiers with global estimators.

Contribution

It develops a novel distributed Bayesian learning method using graphical models and Empirical Bayes, enabling agents to estimate their states from social interaction data.

Findings

Distributed estimators perform well in social interaction scenarios.

The approach effectively combines local Bayesian classifiers with global ML estimators.

Graphical models provide insight into dependencies among scores and states.

Abstract

We consider a network scenario in which agents can evaluate each other according to a score graph that models some interactions. The goal is to design a distributed protocol, run by the agents, that allows them to learn their unknown state among a finite set of possible values. We propose a Bayesian framework in which scores and states are associated to probabilistic events with unknown parameters and hyperparameters, respectively. We show that each agent can learn its state by means of a local Bayesian classifier and a (centralized) Maximum-Likelihood (ML) estimator of parameter-hyperparameter that combines plain ML and Empirical Bayes approaches. By using tools from graphical models, which allow us to gain insight on conditional dependencies of scores and states, we provide a relaxed probabilistic model that ultimately leads to a parameter-hyperparameter estimator amenable to distributed computation. To highlight the appropriateness of the proposed relaxation, we demonstrate the distributed estimators on a social interaction set-up for user profiling.