Document - A novel approach to evaluate community detection algorithms on ground truth

2016

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A novel approach to evaluate community detection algorithms on ground truth

Rossetti G., Pappalardo L., Rinzivillo S.

Classification Complex Networks Community Discovery

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.

Source: Complex Networks VII. Proceedings of the 7th Workshop on Complex Networks, pp. 133–144, Dijon, France, 23-25 March 2016

Publisher: Springer, Berlin , Germania

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@inproceedings{oai:it.cnr:prodotti:366990,
	title = {A novel approach to evaluate community detection algorithms on ground truth},
	author = {Rossetti G. and Pappalardo L. and Rinzivillo S.},
	publisher = {Springer, Berlin , Germania},
	doi = {10.1007/978-3-319-30569-1_10},
	booktitle = {Complex Networks VII. Proceedings of the 7th Workshop on Complex Networks, pp. 133–144, Dijon, France, 23-25 March 2016},
	year = {2016}
}