A thoroughly-validated canonical example problem hasn't emerged just yet, but there are many intersecting domains... Temporal graph analysis of "evolving graph

A thoroughly-validated canonical example problem hasn't emerged just yet, but there are many intersecting domains... Temporal graph analysis of "evolving graphs" is an active research field with some strong motivating use-cases, for instance: profiling networks of fraudulent transactions across N bank accounts with data pulled from M source systems. This paper discusses the analysis of research citations over time, as another example: https://pdfs.semanticscholar.org/110b/0db484a1303eda30aa7e347b8b12e5adc457.pdf&ved=2ahUKEwjOt-mD-oLrAhWUo3EKHdYQCKgQFjABegQIAhAB https://pdfs.semanticscholar.org/110b/0db484a1303eda30aa7e34... That said, Crux's indexes aren't optimal for making all kinds of analytical time-range queries efficient just yet. Instead Crux is currently focussed on point-in-time queries, but the temporal R&D is still happening as it feels very ripe. Looking for examples more generally, I think wherever you have a meaningful use-case for a graph database you probably, eventually, will want to capture and model history. If you then find yourself with two or more such databases that you want to integrate, then you will greatly benefit from a bitemporal graph DBMS. As a fun example, I like to envisage integrating our two federated evolving knowledge graphs. Imagine a tool for "networked thought" like Roam Research that could allow us both to visualise the evolving connections between our independently recorded thoughts, before, during and after this conversation. Graphs of knowledge encoded in time.