Single-trajectory map equation
arXiv:2203.04044 · doi:10.1038/s41598-023-33880-y
Abstract
Community detection, the process of identifying module structures in complex systems represented on networks, is an effective tool in various fields of science. The map equation, which is an information-theoretic framework based on the random walk on a network, is a particularly popular community detection method. Despite its outstanding performance in many applications, the inner workings of the map equation have not been thoroughly studied. Herein, we revisit the original formulation of the map equation and address the existence of its ``raw form,'' which we refer to as the single-trajectory map equation. This raw form sheds light on many details behind the principle of the map equation that are hidden in the steady-state limit of the random walk. Most importantly, the single-trajectory map equation provides a more balanced community structure, naturally reducing the tendency of the overfitting phenomenon in the map equation.
20 pages, 14 figures
References in corpus (8)
- Fast unfolding of communities in large networks
- Maps of random walks on complex networks reveal community structure
- Resolution limit in community detection
- Hierarchical structure and the prediction of missing links in networks
- Community detection in networks: A user guide
- Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems
- Phase transition in the detection of modules in sparse networks
- Mapping higher-order network flows in memory and multilayer networks with Infomap