Discovering Overlapping Communities by Clustering Local Link Structures

Recent advances point out that the existing community detection methods commonly face two challenges: incorrect base‐structures and incorrect membership of weak‐ties. To overcome both problems, a Local link structure (LLS) clustering based method for overlapping community detection is proposed. We extend the similarity of a pair of links to a group of links named LLS, and thus transform mining LLSs as a pattern mining problem. We prove that LLS with an appropriate threshold can filter weak‐ties in the form of bridge and local bridge with its span being larger than 3. A compositive framework is presented for overlapping community detection based on LLS mining and clustering. Comparative experiments on both synthetical and real‐world networks demonstrate that our method has advantage over six existing methods on discovering higher quality communities.