2014/07/23 by Marta Sarzynska, Sarzynska, Marta, Elizabeth A. Leicht +6
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · #Adaptation and Self-Organizing Systems (nlin.AO) #Biological Physics (physics.bio-ph) #COVID-19 epidemiological studies #Complex Network Analysis Techniques #Data Visualization and Analytics #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Populations and Evolution (q-bio.PE) #Social and Information Networks (cs.SI) #cs.SI #nlin.AO #physics.bio-ph #physics.soc-ph #q-bio.PE
paper · pdf · doi:10.48550/arxiv.1407.6297
openalex publication_date 2014/07/23 · arxiv created 2015/01/22 · arxiv updated 2015/01/23 · openalex created_date 2022/08/14 · openalex updated_date 2026/08/01
In the study of networks, it is often insightful to use algorithms to determine mesoscale features such as "community structure", in which densely connected sets of nodes constitute "communities" that have sparse connections to other communities. The most popular way of detecting communities algorithmically is to optimize the quality function known as modularity. When optimizing modularity, one compares the actual connections in a (static or time-dependent) network to the connections obtained from a random-graph ensemble that acts as a null model. The communities are then the sets of nodes that are connected to each other densely relative to what is expected from the null model. Clearly, the process of community detection depends fundamentally on the choice of null model, so it is important to develop and analyze novel null models that take into account appropriate features of the system under study. In this paper, we investigate the effects of using null models that take incorporate spatial information, and we propose a novel null model based on the radiation model of population spread. We also develop novel synthetic spatial benchmark networks in which the connections between entities are based on distance or flux between nodes, and we compare the performance of both static and time-dependent radiation null models to the standard ("Newman-Girvan") null model for modularity optimization and a recently-proposed gravity null model. In our comparisons, we use both the above synthetic benchmarks and time-dependent correlation networks that we construct using countrywide dengue fever incidence data for Peru. We also evaluate a recently-proposed correlation null model, which was developed specifically for correlation networks that are constructed from time series, on the epidemic-correlation data.