TABLE A5.
Methods available in the ‘graph’ submodule
| Method | Short description |
|---|---|
| Graph diffusion distance (GDD) | The GDD metric is a measure of distance between two (positive) weighted graphs based on the Laplacian exponential diffusion kernel. The notion backing this metric is that two graphs are similar if they emit comparable patterns of information transmission. |
| Ipsen‐Mikhailov distance (IMD) | Given two graphs, this method quantifies their difference by comparing their spectral densities. This spectral density is computed as the sum of Lorentz distributions (Ipsen, 2004; Donnat & Holmes 2018). |
| Laplacian energy (LE) | Laplacian graph energy is a measure of graph complexity (Gutman, 2006). |
| Normalised mutual information (NMI) | Normalised Mutual Information proposed (Strehl & Ghosh, 2002) as an extension to Mutual Information to enable interpretations and comparisons between two partitions. |
| Multilayer participation coefficient (MPC) | Multilayer Participation Coefficient method from Guillon (Guilon et al., 2017) using the γ coefficient. |
| Nodal global efficiency (NGE) | The global efficiency as computed per node (Latora & Marchiori, 2001; Latora & Marchiori, 2003) |
| Spectral Euclidean distance (SED) | The spectral distance between graphs is simply the Euclidean distance between the spectra (Wilson & Zhu, 2008). |
| Spectral K distance (SKD) | Given two, we can use their k‐largest positive eigenvalues of their Laplacian counterparts to compute their distance (Jakobson & Rivin, 2000; Rincombe, 2006). |
| Variation of information (VI) | Variation of Information (Meilă, 2007) is an information‐theoretic criterion for comparing two partitions. It is based on the classic notions of entropy and mutual information. In a nutshell, VI measures the amount of information lost or gained in changing between two clusters. VI is a true metric, is always non‐negative and symmetric. |
| Threshold (k‐core decomposition, MST—Mean degree, mean degree, shortest paths, global‐cost efficiency, OMST global‐cost efficiency) |