2011/01/01 by Günce Keziban Orman, Günce Orman, Vincent Labatut +1 · 1 citation
Computer Science · Physics and Astronomy · Psychology · #Complex Network Analysis Techniques #Complex network #Field (mathematics) #Mental Health Research Topics #Opinion Dynamics and Social Influence #Partition (number theory) #Qualitative analysis #Rest (music) #Similarity (geometry) #Similitude #cs.CV #cs.SI #physics.soc-ph
paper · pdf · doi:10.1007/978-3-642-22027-2_23
published as Communications in Computer and Information Science, 167:265-279, 2011 · DICTAP 2011, The International Conference on Digital Information and Communication Technology and its Applications, Dijon : France (2011)
openalex publication_date 2011/01/01 · arxiv created 2012/07/16 · arxiv updated 2012/08/16 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
Community detection is a very active field in complex networks analysis, consisting in identifying groups of nodes more densely interconnected relatively to the rest of the network. The existing algorithms are usually tested and compared on real-world and artificial networks, their performance being assessed through some partition similarity measure. However, artificial networks realism can be questioned, and the appropriateness of those measures is not obvious. In this study, we take advantage of recent advances concerning the characterization of community structures to tackle these questions. We first generate networks thanks to the most realistic model available to date. Their analysis reveals they display only some of the properties observed in real-world community structures. We then apply five community detection algorithms on these networks and find out the performance assessed quantitatively does not necessarily agree with a qualitative analysis of the identified communities. It therefore seems both approaches should be applied to perform a relevant comparison of the algorithms.