2010/12/31 by Paul Expert, Tim Evans, Vincent D. Blondel +1 · 4 citations
Computer Science · Mathematics · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Complex network #Computer network #Computer science #Distributed computing #Focus (optics) #Function (biology) #Human Mobility and Location-Based Analysis #Mathematics #Mobile phone #Modularity (biology) #Network topology #Opportunistic and Delay-Tolerant Networks #Phone #Space (punctuation) #Telecommunications #The Internet #Theoretical computer science #Topology (electrical circuits) #World Wide Web #cs.SI #physics.soc-ph
paper · pdf · doi:10.1073/pnas.1018962108
published as PNAS 2011 108 (19) 7663-7668 · This preprint version combines paper and supplemenatary material of published version. Original title "Beyond Space For Spatial Networks" changed for journal
openalex publication_date 2011/04/25 · arxiv created 2012/01/03 · arxiv updated 2012/01/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Many complex systems are organized in the form of a network embedded in space. Important examples include the physical Internet infrastructure, road networks, flight connections, brain functional networks, and social networks. The effect of space on network topology has recently come under the spotlight because of the emergence of pervasive technologies based on geolocalization, which constantly fill databases with people's movements and thus reveal their trajectories and spatial behavior. Extracting patterns and regularities from the resulting massive amount of human mobility data requires the development of appropriate tools for uncovering information in spatially embedded networks. In contrast with most works that tend to apply standard network metrics to any type of network, we argue in this paper for a careful treatment of the constraints imposed by space on network topology. In particular, we focus on the problem of community detection and propose a modularity function adapted to spatial networks. We show that it is possible to factor out the effect of space in order to reveal more clearly hidden structural similarities between the nodes. Methods are tested on a large mobile phone network and computer-generated benchmarks where the effect of space has been incorporated.