A novel approach to evaluate community detection algorithms on ground truth

Giulio Rossetti, Luca Pappalardo and Salvatore Rinzivillo
(CompleNet 2016 – to appear)

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). We proposed a novel approach aimed at evaluate 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. By defining a classification problem on the real community label we compute an average F1-score that captures the level of approximation reached by network partitions obtained through community discovery algorithms w.r.t. ground-truth ones. Moreover, our approach allows for a visual inspection of the partition quality exploiting density scatter plots.

Fig. 1: Example on the LiveJournal dataset