2011/12/31 by Federica Cerina, V. De Leo, Vincenzo De Leo +3
Computer Science · Mathematics · Physics and Astronomy · Social Sciences · #Artificial intelligence #Block (permutation group theory) #Community structure #Complex Network Analysis Techniques #Component (thermodynamics) #Computer science #Construct (python library) #Data mining #Feature (linguistics) #Human Mobility and Location-Based Analysis #Mathematics #Node (physics) #Opinion Dynamics and Social Influence #Process (computing) #Simple (philosophy) #Space (punctuation) #Statistics #Theoretical computer science #cs.SI #physics.soc-ph
paper · pdf · doi:10.1371/journal.pone.0037507
published as PLoS ONE 7(5): e37507 (2012) · 10 pages and 7 figures
openalex publication_date 2012/05/29 · arxiv created 2012/05/31 · arxiv updated 2012/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Community detection is an important tool for exploring and classifying the properties of large complex networks and should be of great help for spatial networks. Indeed, in addition to their location, nodes in spatial networks can have attributes such as the language for individuals, or any other socio-economical feature that we would like to identify in communities. We discuss in this paper a crucial aspect which was not considered in previous studies which is the possible existence of correlations between space and attributes. Introducing a simple toy model in which both space and node attributes are considered, we discuss the effect of space-attribute correlations on the results of various community detection methods proposed for spatial networks in this paper and in previous studies. When space is irrelevant, our model is equivalent to the stochastic block model which has been shown to display a detectability-non detectability transition. In the regime where space dominates the link formation process, most methods can fail to recover the communities, an effect which is particularly marked when space-attributes correlations are strong. In this latter case, community detection methods which remove the spatial component of the network can miss a large part of the community structure and can lead to incorrect results.