A label propagation algorithm for community detection on high‐mixed networks

Abstract

Community detection on high‐mixed networks has been a challenging problem for complex network researchers. In a Lancichinetti–Fortunato–Radicchi (LFR) network with a mixing parameter mu greater than or equal to 0.5, the quality of the communities partitioned by currently available algorithms will decrease rapidly with increasing mu. To address this issue, we propose a label propagation algorithm on high‐mixed networks, called LPA‐HM, for community detection. In our algorithm, the initial node labels are preprocessed using the number of common neighbors of the nodes, which greatly reduces the initial number of labels and thus improves the quality of the subsequent label propagation process. During the label propagation stage, each node is given the label that is shared by the maximum number of its neighbors. If there are several labels that meet this requirement, the influence of the labels' nodes is calculated, and the label with the maximum total influence is selected as the label of the current node. Early stop conditions based on modularity and run‐to‐run changes in the number of detected communities are incorporated in the algorithm to prevent label overpropagation. The communities that fail to satisfy the definition of weak communities are merged with their most similar neighboring communities. In experiments based on real networks and LFR networks, it is found that the LPA‐HM algorithm is well suited to community detection in a variety of networks. In a high‐mixed LFR network with mu = 0.7, the NMI measure of the LPA‐HM algorithm's community detection performance is still greater than 0.9.