Distributed Learning from Interactions in Social Networks
This work addresses distributed state estimation in social networks, offering a method for user profiling, but it appears incremental as it builds on existing Bayesian and graphical model techniques.
The paper tackles the problem of distributed learning of agent states from interaction scores in social networks, proposing a Bayesian framework with a relaxed probabilistic model that enables distributed computation, and demonstrates its effectiveness in a user profiling setup.
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.