On 11.08.2014 00:07, de wrote: > Looking at the code(graph_community.hh ), I saw that the SA mechanism is not > separated from the energy function itself ( that Newman with gamma=1, you > give here > <https://graph-tool.skewed.de/static/doc/community.html#graph_tool.community.community_structure> > ). > > I see no reason for this , as SA algo could be used with many energy > functions.
This is true, but the speed of the updates will depend crucially on the function used. Modularity has the convenient property that the update of a single node can be done with a complexity O(k), where k is the degree of the node. This feature is exploited in the code to make it fast, and and a "general" version of the code would be much slower. > I hope in the future you can fix it, and even maybe somehow give the > user an API to set the energy function(?) from the python code. Although something like this could be convenient, it would be horribly slow, besides what I mentioned above, since it would involve a Python function call for every node update. It would essentially nullify the advantages of having a C++ code. Furthermore, I don't really have a desire to support every community detection method out there. Not only would this be a lot of work, but also most of these methods are really very bad; and this includes modularity maximization itself as a matter of fact. Having said that, I do intend at some point to include some (one or two) of the most used ones. However, what I would recommend for most people are the methods based on statistical inference of generative models, such as the stochastic block model. This approach lacks most of the problems which plagues modularity and other ad-hoc methods (statistical significance, lack of 'resolution limit', etc). The library currently includes vary fast code for this. The only situation I can imagine where something else should be used, is when doing a comparison of algorithms. If you just want to model some network data, this method should be preferred. Best, Tiago -- Tiago de Paula Peixoto <[email protected]>
signature.asc
Description: OpenPGP digital signature
_______________________________________________ graph-tool mailing list [email protected] http://lists.skewed.de/mailman/listinfo/graph-tool
