# Improved Algorithm for the Network Alignment Problem with Application to Binary Diffing

arXiv: https://arxiv.org/abs/2112.15336 · HTML: https://mlsanity.com/paper/2112.15336/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 31 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [Loopy Belief Propagation for Approximate Inference: An Empirical Study](https://mlsanity.com/paper/1301.6725/) (2013)
- [Graph Matching: Relax at Your Own Risk](https://mlsanity.com/paper/1405.3133/) (2014)
- [KerGM: Kernelized Graph Matching](https://mlsanity.com/paper/1911.11120/) (2019)
- [Spectral Alignment of Graphs](https://mlsanity.com/paper/1602.04181/) (2016)
- [Rigid and Articulated Point Registration with Expectation Conditional Maximization](https://mlsanity.com/paper/2012.05191/) (2020)

## Abstract (arXiv)

In this paper, we present a novel algorithm to address the Network Alignment problem. It is inspired from a previous message passing framework of Bayati et al. [2] and includes several modifications designed to significantly speed up the message updates as well as to enforce their convergence. Experiments show that our proposed model outperforms other state-of-the-art solvers. Finally, we propose an application of our method in order to address the Binary Diffing problem. We show that our solution provides better assignment than the reference differs in almost all submitted instances and outline the importance of leveraging the graphical structure of binary programs.