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Finally, we define the vector ι as a (sum _{g=1}^{M}{N_{g}}times 1) vector of ones and the matrix (boldsymbol {L}=diagleft {boldsymbol {iota }_{g}right }_{g=1}^{g=M}), as an (sum _{g=1}^{M}{N_{g}}times M) matrix with each column being an indicator for being in a particular network.
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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CEO of Professional Science Editing for Scientists @ prosciediting.com