Supracentrality: Centrality Analysis for Multilayer, Multiplex and Temporal Networks. This Python code implements the experimental results described in: Case studies in network community detection. Community structure describes the organization of a network into subgraphs that contain a prevalence of edges within each subgraph and relatively few edges across boundaries between subgraphs. The development of community-detection methods has occurred across disciplines, with numerous and varied algorithms proposed to find communities. As we present in this Chapter via several case studies, community detection is not just an ”end game” unto itself, but rather a step in the analysis of network data which is then useful for furthering research in the disciplinary domain of interest. These case-study examples arise from diverse applications, ranging from social and political science to neuroscience and genetics, and we have chosen them to demonstrate key aspects of community detection and to highlight that community detection, in practice, should be directed by the application at hand.
Keywords for this software
References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Taylor, Dane; Porter, Mason A.; Mucha, Peter J.: Tunable eigenvector-based centralities for multiplex and temporal networks (2021)
- Weir, William H.; Walker, Benjamin; Zdeborová, Lenka; Mucha, Peter J.: Multilayer modularity belief propagation to assess detectability of community structure (2020)
- Weir, William H.; Emmons, Scott; Gibson, Ryan; Taylor, Dane; Mucha, Peter J.: Post-processing partitions to identify domains of modularity optimization (2017)