We want to represent social engagement inside the graph (userX followed userY on 2012-12-23, userX followed userZ on 2012-12-25, where follow might be any kind of engagement like commenting, watching video...)
The question that we would like to ask is if userX follows userY, will it lead to following userZ. To solve this we would need to take edges starting ending at userY (all followers of userY) and see who of them follow userZ looking at the date difference distribution. We were thinking about 2 approaches, one with dates of engagements are represented as nodes in the graph and the other as edge attributes (see attached). The idea behind dates as nodes was to look for cycles and count the number of traversals, which essentially are the number of days between user following userY and userZ. The approach with dates on edges looks "regular" and database friendly, but poses questions how to implement similar algorithms. To summarize I guess, my question is whether putting more logic onto the edges limit the possibilities of using graph theory algorithms (Jakarta, graph cut, min span) and should one aspire to put as little logic on the edges as possible to achieve this. -- You received this message because you are subscribed to the Google Groups "ArangoDB" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
