# Combinatorial Inference for Graphical Models

arXiv: https://arxiv.org/abs/1608.03045 · HTML: https://mlsanity.com/paper/1608.03045/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2016.

## Built on by (most cited first, 2 of 2)

- [Independence Testing for Bounded Degree Bayesian Network](https://mlsanity.com/paper/2204.08690/) (2022)
- [Testing properties of trees in graphical models with covariance queries](https://mlsanity.com/paper/2605.15996/) (2026)

## Abstract (arXiv)

We propose a new family of combinatorial inference problems for graphical models. Unlike classical statistical inference where the main interest is point estimation or parameter testing, combinatorial inference aims at testing the global structure of the underlying graph. Examples include testing the graph connectivity, the presence of a cycle of certain size, or the maximum degree of the graph. To begin with, we develop a unified theory for the fundamental limits of a large family of combinatorial inference problems. We propose new concepts including structural packing and buffer entropies to characterize how the complexity of combinatorial graph structures impacts the corresponding minimax lower bounds. On the other hand, we propose a family of novel and practical structural testing algorithms to match the lower bounds. We provide thorough numerical results on both synthetic graphical models and brain networks to illustrate the usefulness of these proposed methods.