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

2016

Conference article Restricted

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: STUDIES IN COMPUTATIONAL INTELLIGENCE (PRINT), vol. 644, pp. 133-144. Dijon, France, 23-25 March 2016

Metrics

Back to previous page

Cite as

BibTeX entry

@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},
	doi = {10.1007/978-3-319-30569-1_10},
	booktitle = {STUDIES IN COMPUTATIONAL INTELLIGENCE (PRINT), vol. 644, pp. 133-144. Dijon, France, 23-25 March 2016},
	year = {2016}
}