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 optimization in Manufacturing, route optimization in Transportation, drug discovery in Pharma, and so on. Graph technologies are available, though not quite as widely used yet in comparison with relational databases. Even so, interest in knowledge graph practices has grown recently due to AI applications, given the benefits of leveraging graphs and language models together.
A frequent concern is that graph data gets represented at a low level, which tends to make queries more complicated and expensive. There are few mechanisms available — aside from visualizations — for understanding knowledge graphs at different levels of detail. That is to say, how can we work with graph data in more abstracted, aggregate perspectives? While we can run queries on graph data to compute aggregate measures, we don’t have programmatic means of “zooming out” to consider a large graph the way that one zooms out when using an online map. This leaves enterprise applications, which by definition must contend with the inherently multiscale nature of large scale systems, at a distinct disadvantage for leveraging AI applications.
The following presents a survey of related methods to date for level-of-detail abstractions in graphs, along with indications toward future work.