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

arXiv: https://arxiv.org/abs/2306.15695 · HTML: https://mlsanity.com/paper/2306.15695/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 25 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part I](https://mlsanity.com/paper/1701.06307/) (2017)
- [Dynamics over Signed Networks](https://mlsanity.com/paper/1706.03362/) (2017)
- [Opinion Dynamics of Online Social Network Users: A Micro-Level Analysis](https://mlsanity.com/paper/2011.00864/) (2020)
- [Active Sensing of Social Networks](https://mlsanity.com/paper/1601.05834/) (2016)

## Abstract (arXiv)

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.