Community detection is an important research area in complex networks, for which the existing methods are often inaccurate or inefficient (1) at dealing with large real networks, (2) at dealing with dynamic networks. In this paper, we propose DIS, a localized algorithm for uncovering overlapping community structure in real large-scale networks, and ADIS, an adaptive community update method for dynamic networks. Experiments in large-scale real-world networks demonstrate that DIS achieves competitive performance among the baselines, in particular, DIS is over 100x faster than the global algorithms with better quality, and it obtains much more accurate communities than the local algorithms without utilizing priori information. Experiments in dynamic networks demonstrate that ADIS achieves competitive community structure compared to other dynamic methods.

社区检测是复杂网络中的一个重要研究领域，其现有方法通常不准确或效率低下（1）处理大型真实网络，（2）处理动态网络。在本文中，我们提出了一种DIS，一种用于发现大型网络中重叠社区结构的本地化算法，以及一种用于动态网络的自适应社区更新方法，即ADIS。大规模实际网络中的实验表明，DIS在基线之间达到了竞争性能，特别是DIS的质量比全局算法快100倍以上，并且在不利用先验信息的情况下获得了比本地算法更准确的社区。 。