Community detection plays an important role in the analysis of complex networks. However, overlapping community detection in real networks is still a challenge. To address the problems of pre-input parameters and label redundancy, an improved label propagation algorithm (ILPA) that adopts a method based on the influence factor is proposed in this paper. Theoretical analysis and experimental results on both synthetic and real datasets show that the ILPA detects that the overlapping community has higher accuracy compared to other existing methods.

中文翻译：

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用于重叠社区检测的改进标签传播算法

社区检测在复杂网络的分析中起着重要作用。然而，真实网络中的重叠社区检测仍然是一个挑战。针对预输入参数和标签冗余的问题，本文提出了一种基于影响因素的改进标签传播算法（ILPA）。对合成数据集和真实数据集的理论分析和实验结果表明，与其他现有方法相比，ILPA 检测到重叠社区具有更高的准确性。