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Due to nowadays networks' sizes, the evaluation of a community detectionalgorithm can only be done using quality functions. These functions measuredifferent networks/graphs structural properties, each of them corresponding toa different definition of a community. Since there exists many definitions fora community, choosing a quality function may be a difficult task, even if thenetworks' statistics/origins can give some clues about which one to choose.  In this paper, we apply a general methodology to identify different contexts,i.e. groups of graphs where the quality functions behave similarly. In thesecontexts we identify the best quality functions, i.e. quality functions whoseresults are consistent with expectations from real life applications.

On the evaluation potential of quality functions in community detection for different contexts