Portal
The MaRDI Portal
Your Portal to Open Math Research Data
Welcome to the MaRDI Portal of the NFDI.
Welcome to the MaRDI Portal — your gateway to open mathematical research data. Access our comprehensive MaRDI knowledge graph integrating data from sources like DLMF, CRAN, PolyDB, swMATH (partially), zbMATH (partially). Whether you are a researcher, a student, or simply someone passionate about mathematics, the MaRDI Portal is here to support your exploration, learning, and discovery with resources tailored to diverse needs and interests.
We embrace the open-source philosophy — our source code repositories and detailed technical documentation are freely accessible. Or, maybe you want to read about our personas - some fictional characters that embody the distinct goals, motivations, and challenges of our users. If you have further questions or recommendations, you can find ways to contact us here.
The MaRDI project is organized into the following task areas:
- Research Areas
- Outreach
- TA5: The MaRDI Portal
- TA6: Data Culture and Community Integration
- Governance
- TA7: Governance and Consortium Management
Some Example Queries
- Content Pages
- Communities
- DLMF formulae
- swMATH software
- Examples through our Scholia extension:
Discover today's highlight:
Model-based clustering of multiple networks with a hierarchical algorithm
Summary:
This paper introduces a hierarchical algorithm for clustering multiple networks, even when these networks vary in size and do not share the same vertices. The method uses a statistical model-based approach, leveraging stochastic block models (SBMs) to group networks with similar topological structures. Clustering is achieved by maximizing the integrated classification likelihood (ICL) criterion, with an automated selection of the optimal number of clusters. A novel technique is presented to address label-switching issues in SBMs by comparing graphons, enabling accurate aggregation of clusters. The method is evaluated on synthetic data and applied to ecological food web networks, demonstrating its efficiency, interpretability, and robustness compared to existing graph clustering approaches.
Easy summary:
This paper explains a way to group networks, like maps of connections between people or animals, based on how their structure is similar. It uses a smart math-based method called stochastic block models (SBMs) to figure out these groups automatically. The process builds a tree-like diagram (dendrogram) to show how the networks are connected and picks the best number of groups without guessing. A special trick compares parts of the networks to make sure the grouping is accurate, even if the networks are labeled differently. This method was tested on fake data and real examples, like food chains in nature, and worked better than older techniques.
Read more about it on the MaRDI portal: https://portal.mardi4nfdi.de/wiki/Publication:57414
Read more about it on arXiv: https://doi.org/10.48550/arXiv.2211.02314
