Local-first algorithms for community detection

One of the most important problems in the field of social network analysis, and one of the most discussed ones, is community detection, aimed at clustering the nodes on the basis of their social relationships. Community detection is relevant in various fields, including: recommendation systems, link prediction and suggestion, epidemic spreading and information diffusion, sybil detection. In this paper, we discuss various ego-based community detection algorithms and propose a new one, named PaNDEMON, to exploit the parallelism of modern architectures. Comparing its performances with other algorithms, we show that PaNDEMON demonstrates good scalability, while preserving the quality of results.