A Community Detection Algorithm Based on Balanced Label Propagation

OBJECTIVES: In conventional label propagation algorithms, the randomness inherent in the selection order of nodes and subsequent label propagation frequently leads to instability and reduces the accuracy of community detection outcomes. METHODS: First, select the initial node according to the node importance and assign different labels to each initial node, aiming to reduce the number of iterations of the algorithm and improve the efficiency and stability of the algorithm; second, identify the neighbor node with the largest connection to each initial node for the pre-propagation of the labels; then, the algorithm traverses the nodes in descending order of the node importance for the propagation of labels to reduce the randomness of the label propagation process; finally, the final community is formed through the rapid merging of small communities. RESULTS: The experimental results on multiple real datasets and artificially generated networks show that the stability and accuracy are all improved. CONCLUSION: The proposed community detection algorithm based on balanced label propagation is better than the other four advanced algorithms on Q and NMI values of community division results.