01480nas a2200121   4500008004100000245010100041210006900142300001100211490000600222520104400228100002101272856006501293       2020                        eng d00aANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks0 aANGEL efficient and effective nodecentric community discovery in  a1–230 v53 aCommunity 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.1 aRossetti, Giulio  uhttps://link.springer.com/article/10.1007/s41109-020-00270-6