rapport : NA echange AUC et ARI pour cohérence + début interp
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@ -61,24 +61,46 @@ and we store the same predictions.
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\emph{Area Under the Curve} (AUC) for predicted versus real link values and the
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\emph{Area Under the Curve} (AUC) for predicted versus real link values and the
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ARI for predicted versus real block memberships.
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ARI for predicted versus real block memberships.
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\begin{figure}[ht]
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\begin{figure}[H]
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\centering
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\includestandalone{tikz/simulations/na_robustness/auc-model}
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\caption{}
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\label{fig:auc-plot}
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\end{figure}
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\begin{figure}[ht]
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\centering
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\centering
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\includestandalone{tikz/simulations/na_robustness/ari-dim-model}
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\includestandalone{tikz/simulations/na_robustness/ari-dim-model}
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\caption{}
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\caption{ARI in function of $p_\texttt{NA}$, the proportion of missing links
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for various colBiSBM models and their LBM counterparts}
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\label{fig:ari-dim-plot-na}
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\label{fig:ari-dim-plot-na}
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\end{figure}
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\end{figure}
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\begin{figure}[H]
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\centering
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\includestandalone{tikz/simulations/na_robustness/auc-model}
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\caption{AUC in function of $p_\texttt{NA}$, the proportion of missing links
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for various colBiSBM models and their LBM counterparts}
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\label{fig:auc-plot}
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\end{figure}
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\paragraph{Results}
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\paragraph{Results}
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Figures~\ref{fig:auc-plot} and~\ref{fig:ari-dim-plot-na} show a
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Figures~\ref{fig:auc-plot} and~\ref{fig:ari-dim-plot-na} show a
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box plot named \enquote{sep-$model$} that
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box plots named \enquote{sep-$model$} that
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corresponds to the results given by a LBM fitted on data generated with the
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corresponds to the results given by a LBM fitted on data generated with the
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corresponding \emph{model}. These sep box plots are there to serve as a baseline
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corresponding \emph{model}. We will compare the results for one model box plot
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to compare model by model.
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to the corresponding sep-model box plot, serving as a baseline.
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% TODO the ARI interpretation
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For the figure~\ref{fig:ari-dim-plot-na}, in almost all cases
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For the figure~\ref{fig:auc-plot}, overall we observe results similar to
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the ARIs, namely our models tend to have a slightly better AUC than their
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corresponding LBM.
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This indicates that link prediction benefits from the collection model
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in almost all cases.
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We may even be able to improve the results by using larger collections.
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For the cases where our models do not perform better, we observe that
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$\pi\rho$-colBiSBM in
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the modular case seems to do slightly worse than its LBM counterpart for the
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first values of $p_{\texttt{NA}}$. The $\rho$-colBiSBM seem to suffer from the
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same problem, and encounters it too for some values with the nested structure.
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This may have to do with our simulation parameters giving for this models
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cases that are harder.
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Or this may be due to mis-attribution of the block memberships resulting in
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wrong predictions.
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@ -1,4 +1,4 @@
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% Created by tikzDevice version 0.12.6 on 2024-07-24 16:25:30
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% Created by tikzDevice version 0.12.6 on 2024-07-24 17:05:02
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% !TEX encoding = UTF-8 Unicode
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% !TEX encoding = UTF-8 Unicode
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\documentclass[10pt]{standalone}\usepackage{tikz}
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\documentclass[10pt]{standalone}\usepackage{tikz}
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