01443nas a2200133   4500008004100000245008000041210006900121260003500190520094100225100002101166700002101187700001801208856008301226       2016                        eng d00aA novel approach to evaluate community detection algorithms on ground truth0 anovel approach to evaluate community detection algorithms on gro  aDijon, FrancebSpringer-Verlag3 aEvaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating the adherence of a community partition to the ground truth: our methodology provides more information than the state-of-the-art ones and is fast to compute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.1 aRossetti, Giulio1 aPappalardo, Luca1 aRinzivillo, S  uhttp://www.giuliorossetti.net/about/wp-content/uploads/2015/12/Complenet16.pdf