The rapid evolution of social networks in recent years has focused the attention of researchers to find adequate solutions for the management of these networks. For this purpose, several efficient algorithms dedicated to the tracking and the rapid detection of the community structure have been proposed. In this paper, we propose a novel density-based approach with dual optimization for tracking community structure of increasing social networks. These networks are part of dynamic networks evolving by adding nodes with their links. The local optimization of the density makes it possible to reduce the resolution limit problem generated by the optimization of the modularity. The presented algorithm is incremental with a relatively low algorithmic complexity, making it efficient and faster. To demonstrate the effectiveness of our method, we test it on social networks of the real world. The experimental results show the performance and efficiency of our algorithm measured in terms of modularity density, modularity, normalized mutual information, number of communities discovered, running time and stability of communities.

中文翻译：

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具有双重优化的基于密度的方法用于跟踪不断增长的社交网络的社区结构

近年来社交网络的快速发展使研究人员的注意力集中在寻找适当的解决方案来管理这些网络。为此，已经提出了几种专用于跟踪和快速检测社区结构的有效算法。在本文中，我们提出了一种新的基于密度的双重优化方法，用于跟踪不断增长的社交网络的社区结构。这些网络是动态网络的一部分，通过添加带有链接的节点来发展。密度的局部优化使得减少模块化优化产生的分辨率极限问题成为可能。所提出的算法是增量算法，算法复杂度相对较低，使其高效且速度更快。为了证明我们方法的有效性，我们在现实世界的社交网络上对其进行测试。实验结果表明我们的算法在模块化密度、模块化、归一化互信息、发现的社区数量、运行时间和社区稳定性方面的性能和效率。