Hi, I am having some trouble reproducing the performance of minimize_nested_blockmodel_dl on large networks as in the publication "Hierarchical block structures and high-resolution model selection in large networks"? Namely, for large graphs, even with verbose=True, no output is generated, but the CPU usage stays at 100% for hours to days. My network contains 1.5 million nodes and a sorted adjacency list per node such that I can choose the top K edges per node as a parameter. I have tried sampling down to 200,000 nodes and K=5, but minimize_nested_blockmodel_dl does not seem to be proceeding with computation. CPU is being used at 100% while running , and there is plenty of free memory.
Are there options that I can use in minimize_nested_blockmodel_dl to improve performance? Other than sampling and limiting K, are there other strategies that I can try? I compiled using the parallel computing option and am using AWS EC2 instances. The largest network that I have been able to get a result for within 24 compute hours on C3 sized EC2 instances has been 100,000 nodes, K=5. (undirected, average degree about 8) -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/Speeding-up-minimize-nested-blockmodel-dl-on-large-networks-tp4025714.html Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com. _______________________________________________ graph-tool mailing list [email protected] http://lists.skewed.de/mailman/listinfo/graph-tool
