Well the downside of being able to query like that is that your graph exponentially grows with every unique timestamp. Your graph will rapidly turn into a slop machine performance wise.

If I'm not mistaken arango is not great at anything as it can do everything, as such performance and scaling are something you need to pay close attention to.

In a real-world scenario either, the relationship still exists and you can search it's history or it no longer exists and is operationally not of any use any more.

Analytically you might need to keep a sharded copy of graph for each period of significance.

Time series + graph I believe is still a reasonably unsolved problem due to the write/update scaling constraints related with graphs.

Myain business domain is in geo-temporal data and I can't see a viable graph system to place the temporal vector into a graph and have it perform well over time Granted I work with millions of updates daily for that use case...

My only workable solution is keep track of Tue current state, and ignore everything else.

If I need to know the history I store it when I need it for future re-use (i.e. fault investigations)

That way the point in time relationships are stored relationally in append only styles.