# Applying graph matching techniques to enhance reuse of plant design information

arXiv: https://arxiv.org/abs/2103.12466 · HTML: https://mlsanity.com/paper/2103.12466/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 75 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [A General Multi-Graph Matching Approach via Graduated Consistency-regularized Boosting](https://mlsanity.com/paper/1502.05840/) (2015)
- [Graph Matching with Anchor Nodes: A Learning Approach](https://mlsanity.com/paper/1804.03715/) (2018)

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

- [Integrating 2D and 3D Digital Plant Information Towards Automatic Generation of Digital Twins](https://mlsanity.com/paper/2104.01854/) (2021)
- [An Industrial Dataset for Scene Acquisitions and Functional Schematics Alignment](https://mlsanity.com/paper/2602.15584/) (2026)

## Abstract (arXiv)

This article investigates how graph matching can be applied to process plant design data in order to support the reuse of previous designs. A literature review of existing graph matching algorithms is performed, and a group of algorithms is chosen for further testing. A use case from early phase plant design is presented. A methodology for addressing the use case is proposed, including graph simplification algorithms and node similarity measures, so that existing graph matching algorithms can be applied in the process plant domain. The proposed methodology is evaluated empirically on an industrial case consisting of design data from several pulp and paper plants.