Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.

中文翻译：

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ANGEL：在静态和动态网络中高效，有效，以节点为中心的社区发现

社区发现是社交网络分析中最具挑战性的任务之一。在过去的几十年中，已经提出了几种算法，目的是识别复杂网络中的社区，每一种算法都在寻找具有不同和独特特征的中尺度拓扑。在如此众多的文献中，以重叠的，以节点为中心的方法代表了一个有趣的社区发现算法家族，该家族旨在分析社交网络数据。在这项工作中，按照这样的研究方向，我们提出了一种Angel算法，该算法旨在降低先前解决方案的计算复杂度，同时确保识别出高质量的重叠分区。我们在合成数据集和真实数据集上都比较了Angel，与针对相同社区定义而设计的最新社区发现算法相反。我们的实验强调了所提出方法的有效性和效率，并被其不断超越已确定竞争对手的能力所证实。