2013/01/05 by Erwan Le Martelot, Martelot, Erwan Le, Chris Hankin +1
Computer Science · Physics and Astronomy · Social Sciences · #Advanced Computing and Algorithms #Complex Network Analysis Techniques #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Physical sciences #Network Security and Intrusion Detection #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.DS #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1301.0955
arXiv admin note: text overlap with arXiv:1204.1002
openalex publication_date 2013/01/05 · arxiv created 2013/02/05 · arxiv updated 2013/02/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many systems can be described using graphs, or networks. Detecting communities in these networks can provide information about the underlying structure and functioning of the original systems. Yet this detection is a complex task and a large amount of work was dedicated to it in the past decade. One important feature is that communities can be found at several scales, or levels of resolution, indicating several levels of organisations. Therefore solutions to the community structure may not be unique. Also networks tend to be large and hence require efficient processing. In this work, we present a new algorithm for the fast detection of communities across scales using a local criterion. We exploit the local aspect of the criterion to enable parallel computation and improve the algorithm's efficiency further. The algorithm is tested against large generated multi-scale networks and experiments demonstrate its efficiency and accuracy.