Hi Alexandre, Thanks for the response! Yes, sorry for the confusion. I think I have a key misunderstanding (I haven't used python before). When you refer to compilation, does python compile the code I write as I write it? Or rather, is the code compiled once I decide to run it, and it's at this step that the memory usage is high due to filtering? For context, the networks that I'm working with have around 400,000 vertices and 300,000 edges. If I tried fairly rudimentary tasks such as computing the degree for each vertex or computing eigenvector centrality, would these require large amounts of RAM (say, 90-100 GB) due the filtering capabilities that graph-tool has? I'm just trying to get an idea of the upper bound of memory requirements that I may need on my server. Thanks, N -- Sent from: https://nabble.skewed.de/