Research on Label Propagation Algorithms Based on Clustering Coefficient

Label propagation algorithm (LPA) is one of the classical community detection algorithms, with high efficiency, quick speed and no need for any prior information. However, it has the disadvantage of poor stability, which causes the detection results to be random. In order to improve the stability of label propagation algorithm, an algorithm with an adjustable parameter based on clustering coefficient and label propagation is proposed in this article. The algorithm is divided into two steps. The first step is to prioritize the nodes according to their degree and clustering coefficient, and initialize the label according to the ranking result. In the process of initializing the label, only the nodes with clustering coefficient in a certain range are selected to filter out the noisy nodes. The second step is based on the first step. In order to avoid randomness, the neighbor nodes are sorted according to their clustering coefficient and degree, the optimal neighbor node is selected to update the label. By applying the algorithm to LFR artificial network data sets and real networks data sets, the results show that the algorithm reduces the randomness of the label propagation algorithm, enhances the stability and accuracy of detection result, and its adjustable parameter make it possible to have a good quality of community division for various types of networks.