Through the use of the GGDminer tool, it aims to discover Graph Generating Dependencies (GGDs) and showcase information about the graph's content. This process involves applying topological and differential constraints to generate meaningful dependencies.

Source code: http://github.com/avantlab/R2PG-DM

Publications:

• L.C. Shimomura, G. Fletcher, & N. Yakovets (2023) ProGGD - Data Profiling on Knowledge Graphs using Graph Generating Dependencies. International Workshop on the Semantic Web. URL link
• W. van Leeuwen, G. Fletcher, N. Yakovets (2023) A General Cardinality Estimation Framework for Subgraph Matching in Property Graphs. IEEE Transactions on Knowledge and Data Engineering, 35(6), 5485–5505. DOI: https://doi.org/10.1109/TKDE.2022.3161328
•  W. van Leeuwen, G. Fletcher, and N. Yakovets (2024) HomeRun: A Cardinality Estimation Advisor for Graph Databases. Proceedings of the 7th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) (GRADES-NDA '24). Association for Computing Machinery, New York, NY, USA, Article 6, 1–9. DOI: https://doi.org/10.1145/3661304.3661902 
• L. C. Shimomura, N. Yakovets, G. Fletcher (2024) Discovering Graph Generating Dependencies for Property Graph Profiling. CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. DOI: https://doi.org/10.1145/3627673.3679764; arXiv: https://doi.org/10.48550/arXiv.2403.17082
• L. C. Shimomura, N. Yakovets, G. Fletcher (2024) Reasoning on property graphs with graph generating dependencies. Information Sciences, Volume 672, 120675. DOI: https://doi.org/10.1016/j.ins.2024.120675