A novel approach to evaluate community detection algorithms on ground truth
| Title | A novel approach to evaluate community detection algorithms on ground truth |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Rossetti, G, Pappalardo, L, Rinzivillo, S |
| Conference Name | 7th Workshop on Complex Networks |
| Publisher | Springer-Verlag |
| Conference Location | 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. |
| URL | http://www.giuliorossetti.net/about/wp-content/uploads/2015/12/Complenet16.pdf |
| DOI | 10.1007/978-3-319-30569-1_10 |
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