A meta-algorithm for finding large k-plexes

Abstract We focus on the automatic detection of communities in large networks, a challenging problem many disciplines (such as sociology, biology, and computer science). Humans tend to associate form families, villages, nations. Similarly, elements real-world networks naturally highly connected groups. A popular model represent such structures is clique, that is, set fully interconnected nodes. However, it has been observed cliques are too strict practice. The k -plex relaxes notion by allowing each node miss up connections. Although -plexes more flexible than cliques, finding them their number greater. In addition, most small not significant. this paper we tackle only (i.e., comparable size largest clique) design meta-algorithm can be used top known enumeration algorithms return significant fraction time. Our approach relies on: (1) methods for strongly reducing search space (2) decomposition techniques based efficient computation maximal cliques. demonstrate experimentally equipped with our run orders magnitude faster full enumeration.