The study of community structure is of great significance when analyzing the structural and functional characteristics of networks. Attractor is a fast community detection method with the advantage of high accuracy for complex networks. However, in the connected nodes interaction model proposed by the Attractor algorithm, there is a problem with slow convergence during the distance updating process. To solve this problem, we propose an improved Attractor algorithm based on the change trend of the distances between connected nodes. We have generally found that distances between connected nodes exhibit a consistent trend. The dynamic distance trend is determined by setting a window of evaluation. The convergence of the Attractor algorithm is accelerated by the consistent change trend. Experiments on datasets for real-world networks and synthetic networks have shown that our proposed algorithm not only maintains high-quality communities, but also reduces the calculation time significantly and greatly improves the speed of the algorithm.

中文翻译：

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一种改进的节点间动态距离定义的社区检测算法

社区结构的研究对于分析网络的结构和功能特征具有重要意义。Attractor 是一种快速的社区检测方法，具有对复杂网络精度高的优势。但是，Attractor算法提出的连通节点交互模型中，存在距离更新过程中收敛速度慢的问题。为了解决这个问题，我们提出了一种改进的基于连接节点之间距离变化趋势的吸引子算法。我们普遍发现，连接节点之间的距离呈现出一致的趋势。通过设置评估窗口来确定动态距离趋势。Attractor 算法的收敛由一致的变化趋势加速。