AN EFFECTIVE AND EFFICIENT LABEL PROPAGATION ALGORITHM FOR NODE UPDATE MECHANISM

The state-of-the-art label propagation algorithm has to randomly updates node and selects label, leading to the monster community and label oscillation problems. To address these, we propose a label comprehensive in-fluence index(CII), which combines the importance of nodes and the similarity between nodes. Based on CII, we propose the similarity-based label propagation algorithm named ISLPA. We performed experiments to evaluate ISLPA on the Lancichinetti-Fortunato-Radicchi artificial benchmark network and a real net-work. Results show that ISLPA improves the community division quality; the normalized mutual information and the modularity of ISLPA were 20% higher than the compared algorithms, respectively. We implemented parallel ISLPA on Spark, and the results show our parallel ISLPA reduced the running time by approximately 50% while guaranteeing community division quality in large networks of more than 100K nodes. Compared with another parallel algorithm, both the NMI and modularity of parallel ISLPA were approximately 20% higher.