Finding and Testing Network Communities by Lumped Markov Chains

Identifying communities (or clusters), namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. Yet, there is a lack of formal criteria for defining communities and for testing their significance. We propose a sharp definition that is based on a quality threshold. By means of a lumped Markov chain model of a random walker, a quality measure called “persistence probability” is associated to a cluster, which is then defined as an “[Image: see text]-community” if such a probability is not smaller than [Image: see text]. Consistently, a partition composed of [Image: see text]-communities is an “[Image: see text]-partition.” These definitions turn out to be very effective for finding and testing communities. If a set of candidate partitions is available, setting the desired [Image: see text]-level allows one to immediately select the [Image: see text]-partition with the finest decomposition. Simultaneously, the persistence probabilities quantify the quality of each single community. Given its ability in individually assessing each single cluster, this approach can also disclose single well-defined communities even in networks that overall do not possess a definite clusterized structure.