An Overlapping Community Discovery Algorithm Based on Cycle Structure and Label Optimization

An overlapping community discovery algorithm integrating cycle structure and label optimization strategies is proposed. Initially, the node superiority index is defined, and the seed node set is selected based on a threshold. Then labels of non-seed nodes are updated through node similarity to decrease the number of initial labels, which helps enhance the stability of the algorithm and reduce computational complexity. Furthermore, based on cycle structure, an initial label updating sequence is generated from the descending order of node cycle ratios, subsequently combining node importance and node similarity to define label updating rules. Moreover, in cases where indicators are identical, the quantity of shared cycles is used to determine the category of labels, thereby increasing the accuracy of community partitioning. Experiments indicate that compared with five other algorithms, the proposed algorithm demonstrates highest up to 64.1% and 41.5% improvement in EQ and NMI.