# A Learning based Branch and Bound for Maximum Common Subgraph Problems

arXiv: https://arxiv.org/abs/1905.05840 · HTML: https://mlsanity.com/paper/1905.05840/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 1 of 1)

- [Self-Localized Collaborative Perception](https://mlsanity.com/paper/2406.12712/) (2024)

## Abstract (arXiv)

Branch-and-bound (BnB) algorithms are widely used to solve combinatorial problems, and the performance crucially depends on its branching heuristic.In this work, we consider a typical problem of maximum common subgraph (MCS), and propose a branching heuristic inspired from reinforcement learning with a goal of reaching a tree leaf as early as possible to greatly reduce the search tree size.Extensive experiments show that our method is beneficial and outperforms current best BnB algorithm for the MCS.