rapport : NA echange AUC et ARI pour cohérence + début interp

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Louis Lacoste 2024-07-24 17:06:41 +02:00
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@ -61,24 +61,46 @@ and we store the same predictions.
\emph{Area Under the Curve} (AUC) for predicted versus real link values and the \emph{Area Under the Curve} (AUC) for predicted versus real link values and the
ARI for predicted versus real block memberships. ARI for predicted versus real block memberships.
\begin{figure}[ht] \begin{figure}[H]
\centering
\includestandalone{tikz/simulations/na_robustness/auc-model}
\caption{}
\label{fig:auc-plot}
\end{figure}
\begin{figure}[ht]
\centering \centering
\includestandalone{tikz/simulations/na_robustness/ari-dim-model} \includestandalone{tikz/simulations/na_robustness/ari-dim-model}
\caption{} \caption{ARI in function of $p_\texttt{NA}$, the proportion of missing links
for various colBiSBM models and their LBM counterparts}
\label{fig:ari-dim-plot-na} \label{fig:ari-dim-plot-na}
\end{figure} \end{figure}
\begin{figure}[H]
\centering
\includestandalone{tikz/simulations/na_robustness/auc-model}
\caption{AUC in function of $p_\texttt{NA}$, the proportion of missing links
for various colBiSBM models and their LBM counterparts}
\label{fig:auc-plot}
\end{figure}
\paragraph{Results} \paragraph{Results}
Figures~\ref{fig:auc-plot} and~\ref{fig:ari-dim-plot-na} show a Figures~\ref{fig:auc-plot} and~\ref{fig:ari-dim-plot-na} show a
box plot named \enquote{sep-$model$} that box plots named \enquote{sep-$model$} that
corresponds to the results given by a LBM fitted on data generated with the corresponds to the results given by a LBM fitted on data generated with the
corresponding \emph{model}. These sep box plots are there to serve as a baseline corresponding \emph{model}. We will compare the results for one model box plot
to compare model by model. to the corresponding sep-model box plot, serving as a baseline.
% TODO the ARI interpretation
For the figure~\ref{fig:ari-dim-plot-na}, in almost all cases
For the figure~\ref{fig:auc-plot}, overall we observe results similar to
the ARIs, namely our models tend to have a slightly better AUC than their
corresponding LBM.
This indicates that link prediction benefits from the collection model
in almost all cases.
We may even be able to improve the results by using larger collections.
For the cases where our models do not perform better, we observe that
$\pi\rho$-colBiSBM in
the modular case seems to do slightly worse than its LBM counterpart for the
first values of $p_{\texttt{NA}}$. The $\rho$-colBiSBM seem to suffer from the
same problem, and encounters it too for some values with the nested structure.
This may have to do with our simulation parameters giving for this models
cases that are harder.
Or this may be due to mis-attribution of the block memberships resulting in
wrong predictions.

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