Knowledge management in House of Graphs

TL;DR

This paper discusses the structure, data management, and quality assurance processes of the House of Graphs, an online database of graphs, emphasizing its role in reliable scientific research.

Contribution

It provides a detailed overview of the knowledge management strategies and data integrity measures implemented in the House of Graphs database.

Findings

Robust data management ensures accuracy and consistency.

Meta-data enhances the utility of graph entries.

Systematic quality control maintains database reliability.

Abstract

The House of Graphs is an online database of graphs which can be accessed at https://houseofgraphs.org/. It serves as a central repository for complete lists of graphs for various graph classes. However, its main feature is a searchable database of so-called "interesting" graphs. The development of the original House of Graphs started in 2010 and it was completely rebuilt in 2021-2022. Each graph in the database is accompanied by a significant amount of meta-data such as a name, drawings, precomputed graph invariants, and comments. Given this volume of information and the importance of reliability in the scientific world, robust data management is essential to ensure accuracy and consistency across the database. In this article, we therefore focus on knowledge management in the House of Graphs and describe the inner workings of the House of Graphs and how we ensure that its data is coherent, qualitative and stable.