@conference {822,
	title = {A novel approach to evaluate community detection algorithms on ground truth},
	booktitle = {7th Workshop on Complex Networks},
	year = {2016},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Dijon, France},
	abstract = {Evaluating 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.},
	doi = {10.1007/978-3-319-30569-1_10},
	url = {http://www.giuliorossetti.net/about/wp-content/uploads/2015/12/Complenet16.pdf},
	author = {Giulio Rossetti and Luca Pappalardo and S Rinzivillo}
}
