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For the familiar networks of science and global air traffic, the organization revealed by the hierarchical map equation is intuitive and anticipated.
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To quantify the performance of our method, we use the relative mutual information [29] and measure how much we learn about the true benchmark partitions by studying the inferred partitions that we get by applying the hierarchical map equation.
We begin by briefly reviewing the map equation, and then introduce the hierarchical map equation, of which our earlier two-level map equation [14], [15] can be seen as a special case.
Here we provide a detailed description of the mathematics of the hierarchical map equation and outline the stochastic and recursive algorithm we have developed to search for the hierarchical partition of a network that minimizes the hierarchical map equation.
The hierarchical map equation can reveal rich multilevel organization in real-world networks.
We then illustrate the mechanics of the hierarchical map equation, and extract and depict the hierarchical structure of several large-scale networks.
Combined with powerful visualizations, the hierarchical map equation provides a useful tool to comprehend the hierarchical organization of large multiscale social and biological systems.
To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation, which reveals multilevel structures in networks.
This generalization yields the hierarchical map equation, which provides a natural answer to three questions: Into how many hierarchical levels is a given network organized?
The caries risk maps show the smoothed RRs (SmRR) for each parish, which were obtained by running the Bayesian hierarchical mapping model in RIF/WinBUGS.
(Here is a link to the following outline in the form of a hierarchical map).
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