For those who might not be familiar, graph subsampling is a method of extracting smaller graphs from a large graph while preserving some notion of overall structure. The subsampling can be focused on finding representative nodes, edges or combinations of those (as well as optimizing for some other local or global properties).<p>It has applications wherever large graphs are encountered - social networks, biological processes etc. An example application of the library could be as follows - you have a very large graph and you want to do spectral clustering. Throwing spectral clustering directly at such large graphs might not be feasible, so one can first perform a node based subsampling and then apply spectral clustering on the smaller graph on the representative nodes found by the subsampling.