Code for dissertation Uncovering Unbiased Meso-scale Structures in Spatial Networks as part of the MSc in Mathematical Modelling and Scientific Computing 2020, Universtity of Oxford. This repository contains Python packages for performing spatially-corrected clustering on networks and methods for plotting the results spatially.
Clustering methods include modularity optimisation by spectral methods, and spatial backbone extraction as in [2] followed by standard modularity optimisaiton and core-periphery detection. Additionally asymmetric core-periphery detection code for directed graphs is included [3]. Spatial benchmark graphs are generated using a number of different spatial null models [1].
dissertation_noappendix.pdf: Dissertation for MSc Mathematical Modelling and Scientific Computing (2020-2021)spatial-clustering/spatial_graphs/spatial_graphs.p: Module for spatial graph classes which are children of NetworkX's Graph and DiGraph classes. Includes class methods for instantiating spatial graph instances from various spatial null models and node partitions.spatial-clustering/notebooks/Classic Modularity Visualisations.ipynb: notebook for generating figures used in dissertation
- Spatial Correlations in Attribute Communities, Cerina et al. (2011), doi:10.1371/ journal.pone.0037507
- Community Detection in Customer Store Networks, Leal Cervantes (2022)
- Core–periphery structure in directed networks, Elliot et al. (2020), http://dx.doi.org/10.1098/rspa.2019.0783