While working on a graph partitioning strategy, I stumbled upon an interesting paper
https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-024-00310-5
So, let me ask, what successful strategies for multi-level partitioning with #RDF named graphs have you applied or know about?
(beyond this paper and what it cites)
Semantic units: organizing knowledge graphs into semantically meaningful units of representation - Journal of Biomedical Semantics

Background In today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles—ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. Results We introduce “semantic units” as a conceptual solution, although currently exemplified only in a limited prototype. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs by adding another layer of triples on top of the conventional data layer. Semantic units and their subgraphs are represented by their own resource that instantiates a corresponding semantic unit class. We distinguish statement and compound units as basic categories of semantic units. A statement unit is the smallest, independent proposition that is semantically meaningful for a human reader. Depending on the relation of its underlying proposition, it consists of one or more triples. Organizing a knowledge graph into statement units results in a partition of the graph, with each triple belonging to exactly one statement unit. A compound unit, on the other hand, is a semantically meaningful collection of statement and compound units that form larger subgraphs. Some semantic units organize the graph into different levels of representational granularity, others orthogonally into different types of granularity trees or different frames of reference, structuring and organizing the knowledge graph into partially overlapping, partially enclosed subgraphs, each of which can be referenced by its own resource. Conclusions Semantic units, applicable in RDF/OWL and labeled property graphs, offer support for making statements about statements and facilitate graph-alignment, subgraph-matching, knowledge graph profiling, and for management of access restrictions to sensitive data. Additionally, we argue that organizing the graph into semantic units promotes the differentiation of ontological and discursive information, and that it also supports the differentiation of multiple frames of reference within the graph.

BioMed Central

Thank you @kvistgaard for pointing out this paper.

In the anti-fraud space, vendors such as Linkurious and GraphAware tend to have UI features for identifying a subgraph as a "condensed node".

I tried to survey some of the available techniques and related work in https://blog.derwen.ai/graph-levels-of-detail-ea4226abba55?gi=f1429cb490c3

Graph Levels of Detail - derwen

Graph-based data is ubiquitous in enterprise, across the industry verticals, and increasingly needed for machine learning use cases. Classic examples include: anti-fraud in Finance, supply network…

derwen
@kvistgaard At Semic I saw a presentation about LDES with DCAT-AP, that uses named graphs to effectively group changes of entities. https://semiceu.github.io/LDES-DCAT-AP-feeds/
The DCAT-AP Feed specification

@redmer @kvistgaard We have a paper under review at this moment on this. I’ll post it here if it gets published 🤓