List of abbreviations.

Many community detection algorithms are inherently stochastic, leading to variations in their output depending on input parameters and random seeds. This variability makes the results of a single run of these algorithms less reliable. Moreover, different clustering algorithms, optimization criteria (e.g., modularity and the Constant Potts model), and resolution values can result in substantially different partitions on the same network. Consensus clustering methods, such as Ensemble Clustering for Graphs (ECG) and FastConsensus, have been proposed to reduce the instability of non-deterministic algorithms and improve their accuracy by combining a set of partitions resulting from multiple runs of a clustering algorithm. In Complex Networks and their Applications 2024, we introduced FastEnsemble, a new consensus clustering method; here we present a more extensive evaluation of this method. Our results on both real-world and synthetic networks show that FastEnsemble produces more accurate clusterings than two other consensus clustering methods, ECG and FastConsensus, for many model conditions. Furthermore, FastEnsemble is fast enough to be used on networks with more than 3 million nodes, and so improves on the speed and scalability of FastConsensus. Finally, we showcase the utility of consensus clustering methods in mitigating the effect of resolution limit and clustering networks that are only partially covered by communities.