Hierarchical community detection benchmark for heterogeneous inter-community connectivity

Abstract

Here, we introduce a new tool for community detection, a generator of networks, which uses parameters to control the structure of created networks. Typically, network scientists designing novel community detection algorithms use synthetically generated benchmarks with community structures that they intend to detect and scale the benchmark networks across size and density. Currently, available benchmarks use generators limited to the properties of the LFR and GLFR networks. We improve on these previous benchmarks with a new hierarchical benchmark, the HGLFR, that preserves the properties of the LFR and GLFR while extending them to include heterogeneous inter-community connectivity. Networks generated by this benchmark are shown to produce networks with structures triggering the resolution limit while maintaining assortative connectivity.