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.

中文翻译：

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用于高混合网络中社区检测的标签传播算法

对于复杂的网络研究人员而言，在高度混合的网络上进行社区检测一直是一个具有挑战性的问题。在混合参数mu大于或等于0.5的Lancichinetti–Fortunato–Radicchi（LFR）网络中，当前可用算法划分的社区的质量将随着mu的增加而迅速下降。。为了解决此问题，我们提出了一种在称为LPA-HM的高混合网络上进行标签传播的算法，以进行社区检测。在我们的算法中，使用节点的公共邻居的数量对初始节点标签进行预处理，这大大减少了标签的初始数量，从而提高了后续标签传播过程的质量。在标签传播阶段，将为每个节点提供最大数量的邻居共享的标签。如果有多个满足此要求的标签，则计算标签节点的影响，然后选择总影响最大的标签作为当前节点的标签。该算法中包含了基于模块化的提前停止条件以及运行中运行的检测到的社区数量的变化，以防止标签过度传播。无法满足弱社区定义的社区将与其最相似的邻近社区合并。在基于真实网络和LFR网络的实验中，发现LPA-HM算法非常适合各种网络中的社区检测。在高度混合的LFR网络中，mu  = 0.7，则LPA-HM算法的社区检测性能的NMI度量仍大于0.9。