A popular method for generating graphs with known community structure is the Lancichinetti-Fortunato-Radicchi (LFR) model. This paper investigates the use of LFR graphs as training data for learning classifiers that discriminates between edges that are 'within' a community and 'between' network communities. We trained linear edge-wise weighted support vector machine classifiers on LFR graphs generated with different amounts of mixing between communities. Results of a comparative experimental analysis show that a classifier trained on a graph with more mixing also work well when tested on LFR benchmark graphs generated using less mixing, while it achieves mixed performance on real-life networks, with a tendency towards finding many communities.
This download also includes:
To use the code you also need to install the matlab/octave interface for LIBLINEAR.
The entry point of the code is src/run_all.m.
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