Community leader and transition probability based LPA

Abstract

Many practice problems can be transformed into complex networks, and complex network community discovery has become a hot research topic in various fields. The classic label propagation algorithm (LPA) can give community partition very quickly, but stability of the algorithm is poor due to random label propagation. To solve this problem, community leader principle is built and transition probability is introduced, a label propagation algorithm based on community leader and transition probability (CTLPA) is proposed. CTLPA selects threatened leaders and their communities according to the community leader principle, and uses the transition probability and the degree of the leader to jointly control the order for community merger, so that the threatened leader continuously devours the communities that threaten him, until a preliminary community partition is formed. To further reduce the number of community, in CTLPA, based on the characteristic of the community structure: close relationship within the community and sparse relationship outside the community, the closest communities are merged, until the final community partition is obtained. The CTLPA is compared with other five classic algorithms on LFR artificially generated networks and several real data sets. The experimental results show that CTLPA is robust in community partition, it always gives the same community partition, while the LPA will give different results from multiple independent runs. The number of community partition and the normalized mutual information (NMI) of the CTLPA are the best in most cases.