The Graph Data Science library has been built for big datasets; graph representation and algorithms are optimized for big graphs. However, for efficiency's sake, all operations are performed in the heap, which is the reason why having an estimation of the memory requirements to run a given algorithm on a given projected graph can be important.
Estimating memory usage with the estimate procedures
GDS algorithms are run on an in-memory projected graph. The library provides helper procedures, which can be used to predict the memory usage required for storing a projected graph and running a given algorithm. These estimations are performed via the estimate execution mode, which can be appended to graph creation or algorithm execution procedures.
Estimating projected graph memory usage
Projected graphs are stored entirely in-memory (in the heap). In order to know how much memory is required to store a projected graph with the given nodes, relationships, and properties...