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w y (G, Y) is an (sum _{g}N_{g}times R) matrix, with the (i,r) t h element being the weighted sum of i's neighbours' outcomes, ({sum _{jneq i}}G_{ij,g}y_{j,g}omega _{j,g}^{r}) or ({sum _{jneq i}}tilde {G}_{ij,g}y_{j,g}omega _{j,g}^{r}), with weights (omega _{j,g}^{r}) being the neighbour's r t h network statistic (e.g. the neighbour's eigenvector centrality in the DeGroot model of social learning).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com