Joint Learning of Network Topology and Opinion Dynamics Based on Bandit Algorithms

TL;DR

This paper introduces a bandit algorithm for jointly learning network topology and diverse opinion update rules, effectively improving network estimation and prediction accuracy in complex social systems.

Contribution

It presents a novel bandit-based learning method for simultaneously inferring network structure and heterogeneous opinion dynamics.

Findings

Improves initial network and rule estimates.

Reduces prediction error.

Outperforms sparse linear and Gaussian process regression.

Abstract

We study joint learning of network topology and a mixed opinion dynamics, in which agents may have different update rules. Such a model captures the diversity of real individual interactions. We propose a learning algorithm based on multi-armed bandit algorithms to address the problem. The goal of the algorithm is to find each agent's update rule from several candidate rules and to learn the underlying network. At each iteration, the algorithm assumes that each agent has one of the updated rules and then modifies network estimates to reduce validation error. Numerical experiments show that the proposed algorithm improves initial estimates of the network and update rules, decreases prediction error, and performs better than other methods such as sparse linear regression and Gaussian process regression.