Knowledge management in House of Graphs

2026-03-24

AI Summary

This work proposes a knowledge management framework tailored for “interesting” graph structures to address challenges concerning data accuracy and stability in the House of Graphs database. By systematically organizing graph data alongside associated metadata—including names, visualizations, precomputed invariants, and annotations—the framework integrates metadata management, invariant precomputation, data validation, and version control to ensure consistency, quality, and long-term maintainability of large-scale graph datasets. Deployed during the comprehensive 2021–2022 overhaul of House of Graphs, this approach significantly enhanced data quality, query efficiency, and overall system robustness.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Ontologies

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data integration and cleaning

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.

Problem

• knowledge management
• graph database
• data consistency
• metadata reliability
• scientific data integrity

Innovation

• knowledge management
• graph database
• metadata consistency
• graph invariants
• data reliability