Detecting communities in time-evolving/dynamic networks is an important operation used in many real-world network science applications. While there have been several proposed strategies for dynamic community detection, such approaches do not necessarily take advantage of the locality of changes. In this paper, we present a new technique called Delta-Screening (or simply, $\Delta$ -screening) for updating communities in a dynamic graph. The technique assumes that the graph is given as a series of time steps, and outputs a set of communities for each time step. At the start of each time step, the $\Delta$ -screening technique examines all changes (edge additions and deletions) and computes a subset of vertices that are likely to be impacted by the change (using the modularity objective). Subsequently, only the identified subsets are processed for community state updates. Our experiments demonstrate that this scheme, despite its ability to prune vertices aggressively, is able to generate significant savings in runtime performance (up to 38× speedup over static baseline and $5 \times$ over dynamic baseline implementations), without compromising on the quality. We test on both real-world and synthetic network inputs containing both edge additions and deletions. The $\Delta$ -screening technique is generic to be incorporated into any of the existing modularity-optimizing clustering algorithms. We tested using two state-of-the-art clustering implementations, namely, Louvain and SLM. In addition, we also show how to use the $\Delta$ -screening approach to delineate appropriate intervals of temporal resolutions at which to analyze a given input network.

中文翻译：

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Delta-Screening：一种快速有效的更新动态图中社区的技术

在时间演化/动态网络中检测社区是许多现实世界网络科学应用中使用的重要操作。虽然已经提出了几种动态社区检测策略，但这些方法不一定利用变化的局部性。在本文中，我们提出了一种称为 Delta-Screening（或简称，$\Delta$ -screening) 用于更新动态图中的社区。该技术假设图形以一系列时间步长的形式给出，并为每个时间步长输出一组社区。在每个时间步开始时，$\Delta$ -筛选技术检查所有更改（边添加和删除）并计算可能受更改影响的顶点子集（使用模块化目标）。随后，仅处理识别的子集以进行社区状态更新。我们的实验表明，尽管该方案能够积极修剪顶点，但能够显着节省运行时性能（比静态基线和$5 \times$ 通过动态基线实现）， 没有在质量上妥协。我们对包含边添加和删除的真实世界和合成网络输入进行测试。这$\Delta$ -筛选技术是通用的，可以合并到任何现有的模块化优化聚类算法中。我们使用两种最先进的集群实现进行了测试，即 Louvain 和 SLM。此外，我们还展示了如何使用$\Delta$ -筛选方法来描绘分析给定输入网络的适当时间分辨率间隔。