554 lines
No EOL
23 KiB
TeX
554 lines
No EOL
23 KiB
TeX
\section{Model Context}
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\label{sec:context-of-the-model}
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\begin{frame}
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\frametitle{Why a network?}
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\begin{columns}
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\begin{column}{0.5\textwidth}
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\begin{columns}
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\begin{column}{0.5\textwidth}
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\begin{figure}[ht]
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\centering
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\begin{tikzpicture}[scale=.6,rotate=270]
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\input{tikz/plantpollinatornetwork.tex}
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\end{tikzpicture}
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\caption{Example of a network}
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\label{fig:plants-pollin}
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\end{figure}
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\end{column}
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\begin{column}{0.3\textwidth}
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\centering
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\begin{align*}
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\begin{pmatrix}
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1 & 0 & 1 \\
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1 & 0 & 0 \\
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1 & 0 & 0 \\
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1 & 1 & 0
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\end{pmatrix}
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\end{align*}
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\footnotesize
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Associated adjacency matrix
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\end{column}
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\end{columns}
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\begin{figure}[ht]
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\centering
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\includegraphics[width=0.7\textwidth]{tikz/applications/baldock/graph-Baldock2019_Bristol.pdf}
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\caption{Plant-pollinator network of
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Bristol\newline\cite{baldockSystemsApproachReveals2019}}
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\label{fig:label}
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\end{figure}
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\end{column}
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\begin{column}{0.5\textwidth}
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\begin{itemize}
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\item Modeling of various interactions, here ecosystems
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\item Structure necessary for: biodiversity monitoring, robustness, risk
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of collapse
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\item Increasingly available
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\end{itemize}
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\end{column}
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\end{columns}
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\end{frame}
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\begin{frame}{Analysis methods for a network}
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Several methods~:
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\begin{itemize}
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\item Metrics~: degree, centrality, nesting \dots
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\item Network embedding with GNN
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\item \textbf<2>{\emph{Clustering} of nodes with latent variable models}
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\end{itemize}
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\end{frame}
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\begin{frame}
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\addtocounter{footnote}{1}
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\frametitle{Latent Block Model (LBM\footnotemark[\thefootnote])}
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%DONE remplacer i \in bullet par Zi = \bullet
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\cite{govaertEMAlgorithmBlock2005}.
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\begin{columns}
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\begin{column}{0.40\linewidth}
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\begin{figure}[H]
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\center
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\begin{tikzpicture}[scale=0.35]
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\input{tikz/lbm.tex}
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\end{tikzpicture}
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\caption{Example of LBM\footnotemark[\thefootnote]}
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\label{fig:LBMvisu}
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\end{figure}
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\end{column}
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\only<1>{
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\begin{column}{0.51\linewidth}
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\begin{block}{Hierarchical model}
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\vspace{-\baselineskip}
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\begin{align*}
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\forall q\in[\![ 1, Q_1]\!],~ & \mathbb{P}(Z_i = q) = \pi_q \\
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\forall r\in[\![ 1, Q_2]\!],~ & \mathbb{P}(W_j = r) = \rho_r \\
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& Y_{ij} | Z_i, W_j \sim \mathcal{F}(\alpha_{Z_i,W_j})
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\end{align*}
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where $|\pi| = Q_1, |\rho| = Q_2, |\alpha| = Q_1 \times Q_2$
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\end{block}
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\begin{block}{Concise LBM formula}
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$Y \sim \mathcal{F}\text{-BiSBM}_{n_1,n_2}(Q_1, Q_2, \pi, \rho, \alpha)$
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\end{block}
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\end{column}}
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\only<2>{
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\begin{column}{0.51\linewidth}
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With \begin{itemize}
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\item $Q_1 = |\{{\color{blueind}\bullet},{\color{cyanind}\bullet},{\color{electricblue}\bullet}\}|$ fixed row blocks
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\item $Q_2 = |\{{\color{burntorange}\bullet},{\color{goldenyellow}\bullet},{\color{peach}\bullet}\}|$ fixed column blocks
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\end{itemize}
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\begin{block}{Parameters}
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\begin{itemize}
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\item $\pi_{{\color{blueind}\bullet}} = \mathbb{P}(Z_i = {\color{blueind}\bullet})$
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\item $\rho_{{\color{burntorange}\bullet}} = \mathbb{P}(W_j = {\color{burntorange}\bullet})$
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\item $\alpha_{{\color{blueind}\bullet}{\color{burntorange}\bullet}} = \mathbb{P}(Y_{ij} = 1 | Z_i = {\color{blueind}\bullet}, W_j = {\color{burntorange}\bullet})$
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\end{itemize}
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\end{block}
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\end{column}}
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\end{columns}
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\footnotetext[\thefootnote]{Which I will henceforth call BiSBM}
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\end{frame}
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\begin{frame}
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\frametitle{Multiple networks}
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\begin{figure}[ht]
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\centering
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\begin{subfigure}[ht]{0.3\textwidth}
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\includegraphics[width=1.1\textwidth]{tikz/applications/baldock/mat-Baldock2019_Bristol.pdf}
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\caption{Bristol}
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\end{subfigure}
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\begin{subfigure}[ht]{0.3\textwidth}
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\includegraphics[width=1.1\textwidth]{tikz/applications/baldock/mat-Baldock2019_Edinburgh.pdf}
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\caption{Edinburgh}
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\end{subfigure}
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\begin{subfigure}[ht]{0.3\textwidth}
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\includegraphics[width=1.1\textwidth]{tikz/applications/baldock/mat-Baldock2019_Leeds.pdf}
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\caption{Leeds}
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\end{subfigure}
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\caption{Adjacency matrices,~\cite{baldockSystemsApproachReveals2019}}
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\label{fig:adj}
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\end{figure}
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\end{frame}
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\section[Bipartite collection models]{Bipartite network collection models}
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\label{sec:extension-of-colsbm-to-bipartite-networks}
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\begin{frame}
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\frametitle{Bipartite collections}
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\[
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\forall m \in \{1\dots M\}, Y^m \overset{ind}{\sim} \mathcal{F}\text{-BiSBM}_{n_1^m,n_2^m}(Q_1^m, Q_2^m, \pi^m, \rho^m, \alpha^m)
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\]
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\onslide<2>{
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\begin{figure}[ht]
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\centering
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\begin{subfigure}[ht]{0.3\textwidth}
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\includegraphics[width=1.1\textwidth]{tikz/applications/baldock/bisbm-mat-Baldock2019_Bristol.pdf}
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\caption{Bristol}
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\end{subfigure}
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\begin{subfigure}[ht]{0.3\textwidth}
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\includegraphics[width=1.1\textwidth]{tikz/applications/baldock/bisbm-mat-Baldock2019_Edinburgh.pdf}
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\caption{Edinburgh}
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\end{subfigure}
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\begin{subfigure}[ht]{0.3\textwidth}
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\includegraphics[width=1.1\textwidth]{tikz/applications/baldock/bisbm-mat-Baldock2019_Leeds.pdf}
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\caption{Leeds}
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\end{subfigure}
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\caption{Reordered adjacency matrices, thanks to LBM}
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\label{fig:adj-reord}
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\end{figure}
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}
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\end{frame}
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\begin{frame}
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\frametitle{Different models}
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\onslide<1->{ \begin{block}{\emph{iid}-colBiSBM}
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\[
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\forall m \in \{1\dots M\}, Y^m \overset{iid}{\sim}
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\mathcal{F}\text{-BiSBM}_{n_1^m,n_2^m}(Q_1, Q_2, \pi, \rho, \alpha)
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\]
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with $\theta = (\pi, \rho, \alpha)$.
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\end{block}}
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\onslide<2>{ \begin{block}{$\pi\rho$-colBiSBM}
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\[
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\forall m \in \{1\dots M\}, Y^m \overset{ind}{\sim}
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\mathcal{F}\text{-BiSBM}_{n_1^m,n_2^m}(Q_1, Q_2, \pi^m, \rho^m, \alpha)
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\]
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with $\theta = ((\pi^m)_{m=1,\dots, M}, (\rho^m)_{m=1,\dots, M}, \alpha)$.
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\end{block}
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}
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\end{frame}
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\begin{frame}
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\frametitle{Parameter estimation}
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% DONE say that tau i q m c' is the probability that Zim = q, approximation of the variational probability. Because we impose independence
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% By maximizing a variational lower bound of the
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% log-likelihood of the observed data.
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Maximizing the log-likelihood?
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\begin{block}{log-likelihood and complete log-likelihood}
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\[
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\ell(\bm{Y};\theta) = \sum_{\bm{Z,W}\in \bm{\mathcal{Z}\times\mathcal{W}}} \ell_c(\bm{Y}, \bm{Z}, \bm{W};\theta)
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\]
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with $\bm{\mathcal{Z}} = \{1,\dots,\alert<2>{Q_1}\}^{\alert<2>{n}},
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\bm{\mathcal{W}} = \{1,\dots,\alert<2>{Q_2}\}^{\alert<2>{n}}$
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\end{block}
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\uncover<3>{So, classic algorithm $\Rightarrow$
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\emph{Expectation-Maximization} (EM).}
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\end{frame}
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\begin{frame}
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\frametitle{By classic EM}
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At iteration $(t)$:
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\begin{itemize}
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\item[$\bullet$]\textbf{E Step}: calculate
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$$ \mathcal{Q}(\theta | \theta^{(t-1)}) = \mathbb E_{\alert<2>{\bm Z, \bm W | \bm Y, \theta^{(t-1)}} } \left[\ell_c(\bm Y, \bm W, \bm Z; \theta) \right] $$
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\item[$\bullet$]\textbf{M Step}:
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$$ \theta^{(t)} = \arg \max_{\theta} \mathcal{Q}(\theta | \theta^{(t-1)})$$
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\end{itemize}
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\uncover<2>{
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\begin{alertblock}{Problem for classic EM}
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Law of $\bm{Z,W|Y},\theta^{(t-1)}$ inaccessible
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\end{alertblock}}
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\end{frame}
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\begin{frame}
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By \emph{Variational EM}, as proposed
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by~\cite{daudinMixtureModelRandom2008,
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chabert-liddellLearningCommonStructures2024}.
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\begin{block}{Variational approximation of $\bm{Z,W|Y},\theta^{(t-1)}$}
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$\mathcal{R}_{Y^m,\tau}(\mathbf{Z}^m, \mathbf{W}^m) =
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\mathcal{R}^1_{Y^m,\tau}(\mathbf{Z}^m)
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{\color{red}\times}
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\mathcal{R}^2_{Y^m,\tau}(\mathbf{W}^m) \Rightarrow$ independence rows, columns.
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\end{block}
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\begin{multline*}
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\ell (\bm{Y};\theta) \geq \color{red}\sum_{m=1}^{M} \bigg(
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\color{black} \mathcal{Q}^m(\theta\mid\theta^{(t)}) +
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\mathcal{H}(\mathcal{R}_{Y^m,\theta^{(t)}}
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(\mathbf{Z}^m, \mathbf{W}^m))
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\color{red}\bigg) \color{black}
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\eqcolon \mathcal{J}(\tau;\theta)
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\end{multline*}
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where $\mathcal{Q}^m(\theta\mid\theta^{(t)}) =
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\mathbb{E}_{\mathbf{Z}^m,\mathbf{W}^m
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\sim \mathcal{R}_{Y^m,\tau}(.)}
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\left[ \ell_c(Y^m,\mathbf{Z}^m,\mathbf{W}^m | \theta) \right] \,$
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\end{frame}
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\begin{frame}{Developed formula of variational EM}
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\begin{multline*}
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\ell (\bm{Y};\theta) \geq \color{red}\sum_{m=1}^{M} \bigg( \color{black} \sum_{i = 1}^{n_1^m}\sum_{j=1}^{n_2^m}\sum_{q \in \mathcal{Q}_{1,m}} \sum_{r \in \mathcal{Q}_{2,m}} \tau^{1,m}_{i,q} \tau^{2,m}_{j,r} \log f(Y^{m}_{ij}; \alpha_{qr}) \\
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+ \sum_{i=1}^{n_1^m} \sum_{q \in \mathcal{Q}_{1,m}} \tau^{1,m}_{i,q} \log \pi_{\color{black}q}^{\color{gray}m} + \sum_{j=1}^{n_2^m} \sum_{r \in \mathcal{Q}_{2,m}} \tau^{2,m}_{j,r} \log \rho_{\color{black}r}^{\color{gray}m} \\
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- \sum_{i=1}^{n_1} \tau^{1,m}_{i,q} \log \tau^{1,m}_{i,q} - \sum_{j=1}^{n_2} \tau^{2,m}_{j,r} \log \tau^{2,m}_{j,r} \color{red}\bigg) \color{black} \eqcolon
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\mathcal{J}(\tau;\theta),
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\end{multline*}
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\begin{block}{Variational approximation}
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$\tau_{iq}^{1,m} = \mathcal{R}^1_{Y^m,\tau}(Z_{iq}^m = 1)$
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and $\tau_{jr}^{2,m} = \mathcal{R}^2_{Y^m,\tau}(W_{jr}^m = 1)$
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\end{block}
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\end{frame}
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\begin{frame}{\emph{Variational Expectation} Step}
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\[
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\widehat{\tau}^{(t+1)} = \arg \max_{\tau}
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\mathcal{J}(\mathcal{\tau},\bm{\widehat{\theta}}^{(t)})
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\Leftrightarrow \arg\min_{\tau\in\mathcal{T}} \mathbf{KL}[\mathcal{R}_{\mathbf{Y},\tau}, \mathbb{P}(.|\mathbf{Y})]
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\]
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\begin{equation*}
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\begin{cases}
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\widehat{\tau}_{iq}^{1,m} \propto \widehat{\pi}_{q}^{m(t)} \prod_{j=1}^{n_2^m}\prod_{r\in\mathcal{Q}_2^m} f(Y_{ij}^m;\widehat{\alpha}_{qr}^{(t)})^{\widehat{\tau}_{jr}^{2,m(t+1)}} & \forall i = 1, \dots , n_1^m, q \in \mathcal{Q}_1^m \\
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\widehat{\tau}_{jr}^{2,m} \propto \widehat{\rho}_{r}^{m(t)} \prod_{i=1}^{n_1^m}\prod_{q\in\mathcal{Q}_1^m} f(Y_{ij}^m;\widehat{\alpha}_{qr}^{(t)})^{\widehat{\tau}_{iq}^{1,m(t+1)}} & \forall j = 1, \dots , n_2^m, r \in \mathcal{Q}_2^m
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\end{cases}
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\end{equation*}
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\footnotetext[2]{Initialization of $\widehat{\tau}$ with a
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\emph{spectral clustering} on the networks.}
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\end{frame}
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\begin{frame}{\emph{Maximization} Step}
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\[
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\widehat{\theta}^{(t+1)} = \arg \max_{\theta} \mathcal{J}(\mathcal{\bm{\widehat{\tau}}}^{(t+1)},\theta)
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\]
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\begin{block}{Connectivity parameters}
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\begin{align*}
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\widehat{\alpha}_{qr} = \frac{\sum_{m=1}^{M} \sum_{i=1}^{n_1^m} \sum_{j=1}^{n_2^m} \tau_{iq}^{1,m} \tau_{jr}^{2,m} \alert<2>{Y_{ij}^m}}{\sum_{m=1}^{M} \sum_{i=1}^{n_1^m} \sum_{j=1}^{n_2^m} \tau_{iq}^{1,m} \tau_{jr}^{2,m}}
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\end{align*}
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\end{block}
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\only<1>{
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\begin{block}{Proportions for \emph{iid}}
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\begin{align*}
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\widehat{\pi}_q = \frac{\sum_{m=1}^{M} \sum_{i=1}^{n_1^m} \tau_{iq}^{1,m}}{\sum_{m=1}^{M} n_1^m} & &
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\widehat{\rho}_r = \frac{\sum_{m=1}^{M} \sum_{j=1}^{n_2^m} \tau_{jr}^{2,m}}{\sum_{m=1}^{M} n_2^m}
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\end{align*}
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\end{block}
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}
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\only<2>{
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\begin{block}{Proportions for $\pi\rho$}
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\begin{align*}
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\widehat{\pi}^{\color{red}m}_q = \frac{\sum_{i=1}^{n_1^m} \tau_{iq}^{1,m}}{n_1^m} & &
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\widehat{\rho}^{\color{red}m}_r = \frac{\sum_{j=1}^{n_2^m} \tau_{jr}^{2,m}}{n_2^m}
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\end{align*}
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\end{block}
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}
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\end{frame}
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\section{Model selection}
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\begin{frame}
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\frametitle{Problem of choosing $(Q_1, Q_2)$}
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Need to select $Q_1$ and $Q_2$. BIC-Like criterion\footnote{ICL + Entropy + penalty}
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\begin{align*}
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\text{BIC-L}(\bm{Y}, Q_1, Q_2) & = \max_{\theta} \mathbb{E}_{\mathcal{R}_{\mathbf{Y},\hat{\tau}}} [\ell_c(\bm{Y,Z,W};\theta)] + \mathcal{H(\mathcal{R}_{\mathbf{Y},\hat{\tau}})} - \frac{1}{2}\text{pen}(\theta, Q_1, Q_2) \\
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& = \max_{\theta} \mathcal{J(\mathcal{R}_{\mathbf{Y},\hat{\tau}}, \theta)} - \frac{1}{2}\text{pen}(\theta, Q_1, Q_2)
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\end{align*}
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\begin{alertblock}{Exploration problems}
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\begin{itemize}
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\item Exploration of $\mathbb{N}^2$ costly.
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\item Sensitivity to initializations.
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\end{itemize}
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\end{alertblock}
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\end{frame}
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\begin{frame}
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\frametitle{Choice of $(Q_1,Q_2)$ - Greedy approach}
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\begin{columns}
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\begin{column}{0.5\linewidth}
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\begin{tikzpicture}
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\input{tikz/greedy-exploration.tex}
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\end{tikzpicture}
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\end{column}
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\begin{column}{0.35\linewidth}
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\begin{itemize}
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\item Initial model~:\\
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\begin{tikzpicture}
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\draw[fill=gray, draw=gray] circle [radius=0.225cm];
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\end{tikzpicture}
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\onslide<2->{
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\item Model after \emph{split}~:
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\begin{tikzpicture}
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\draw[fill=blueind, draw=blueind] circle [radius=0.225cm];
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\end{tikzpicture}
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\item Model maximizing the criterion~:\\
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\begin{tikzpicture}
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\draw[fill=white, draw=green, very thick] circle [radius=0.225cm];
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\end{tikzpicture}
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}
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\onslide<3->{
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\item Model after \emph{merge}~:
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\begin{tikzpicture}
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\draw[fill=red, draw=red] circle [radius=0.225cm];
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\end{tikzpicture}
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}
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\end{itemize}
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\end{column}
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\end{columns}
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\end{frame}
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\begin{frame}
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\frametitle{Choice of $(Q_1,Q_2)$ - Sliding window}
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\begin{columns}
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\begin{column}{0.6\textwidth}
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\begin{figure}
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\input{tikz/moving-window}
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\caption{Sliding window}
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\end{figure}
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\end{column}
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\begin{column}{0.4\textwidth}
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\only<3>{\begin{block}{}
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Initialization of the model if necessary
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\end{block}}
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\only<9>{\begin{block}{}
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Localization of the new mode
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\end{block}}
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\only<10>{\begin{block}{}
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Move to the new mode then iterate
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\end{block}}
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\end{column}
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\end{columns}
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\end{frame}
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\section{Application}
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\label{sec:application}
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\begin{frame}
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\frametitle{Results~\cite{baldockSystemsApproachReveals2019}}
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\only<1>{
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\begin{figure}[ht]
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\centering
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\begin{subfigure}[t]{0.5\textwidth}
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\centering
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\includegraphics[width=0.45\textwidth]{tikz/applications/baldock/mat-Baldock2019_Bristol.pdf}
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\caption{Bristol}
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\end{subfigure}\hfil
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\begin{subfigure}[t]{0.5\textwidth}
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\centering
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\includegraphics[width=0.45\textwidth]{tikz/applications/baldock/mat-Baldock2019_Edinburgh.pdf}
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\caption{Edinburgh}
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\end{subfigure}
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\newline
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\begin{subfigure}[ht]{0.5\textwidth}
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\centering
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\includegraphics[width=0.5\textwidth]{tikz/applications/baldock/mat-Baldock2019_Leeds.pdf}
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\caption{Leeds}
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\end{subfigure}\hfil
|
|
\begin{subfigure}[ht]{0.5\textwidth}
|
|
\centering
|
|
\includegraphics[width=0.5\textwidth]{tikz/applications/baldock/mat-Baldock2019_Reading.pdf}
|
|
\caption{Reading}
|
|
\end{subfigure}
|
|
\caption{Adjacency matrices,~\cite{baldockSystemsApproachReveals2019}}
|
|
\end{figure}
|
|
}
|
|
\only<2>{
|
|
\begin{figure}[ht]
|
|
\centering
|
|
\begin{subfigure}[t]{0.5\textwidth}
|
|
\centering
|
|
\includegraphics[width=0.45\textwidth]{tikz/applications/baldock/colbisbm-mat-Baldock2019_Bristol.pdf}
|
|
\caption{Bristol}
|
|
\end{subfigure}\hfil
|
|
\begin{subfigure}[t]{0.5\textwidth}
|
|
\centering
|
|
\includegraphics[width=0.45\textwidth]{tikz/applications/baldock/colbisbm-mat-Baldock2019_Edinburgh.pdf}
|
|
\caption{Edinburgh}
|
|
\end{subfigure}
|
|
\newline
|
|
\begin{subfigure}[ht]{0.5\textwidth}
|
|
\centering
|
|
\includegraphics[width=0.5\textwidth]{tikz/applications/baldock/colbisbm-mat-Baldock2019_Leeds.pdf}
|
|
\caption{Leeds}
|
|
\end{subfigure}\hfil
|
|
\begin{subfigure}[ht]{0.5\textwidth}
|
|
\centering
|
|
\includegraphics[width=0.5\textwidth]{tikz/applications/baldock/colbisbm-mat-Baldock2019_Reading.pdf}
|
|
\caption{Reading}
|
|
\end{subfigure}
|
|
\caption{Reordered adjacency matrices by \emph{iid}-colBiSBM,~\cite{baldockSystemsApproachReveals2019}}
|
|
\end{figure}
|
|
}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{Network clustering}
|
|
\begin{figure}[ht]
|
|
\includegraphics[width=0.45\textwidth]{tikz/applications/baldock/mat-Baldock2011_TB+Baldock2011_JN.pdf}
|
|
\caption{Adjacency matrix,~\cite{baldockDailyTemporalStructure2011}}
|
|
\end{figure}
|
|
\end{frame}
|
|
|
|
\begin{frame}[allowframebreaks]
|
|
\frametitle{Application to~\cite{baldockDailyTemporalStructure2011,
|
|
baldockSystemsApproachReveals2019}}
|
|
\begin{figure}[t]
|
|
\centering
|
|
\begin{subfigure}{0.5\textwidth}
|
|
\centering
|
|
\includegraphics[scale=0.2,angle=-90]{backup-app-iid.png}
|
|
\caption{Model $iid$}
|
|
\end{subfigure}%
|
|
~
|
|
\begin{subfigure}{0.5\textwidth}
|
|
\centering
|
|
\includegraphics[scale=0.2,angle=-90]{backup-app-pirho.png}
|
|
\caption{Model $\pi\rho$}
|
|
\end{subfigure}%
|
|
\caption{Partitioning of networks
|
|
of~\cite{baldockDailyTemporalStructure2011,
|
|
baldockSystemsApproachReveals2019}}
|
|
\end{figure}
|
|
|
|
\begin{figure}[t]
|
|
\centering
|
|
\begin{subfigure}{0.5\textwidth}
|
|
\centering
|
|
\includegraphics[scale=0.1]{backup-app-iid-struct1.png}
|
|
\includegraphics[scale=0.2]{backup-app-iid-struct2.png}
|
|
\caption{Model $iid$,\\
|
|
separate African network and English networks}
|
|
\end{subfigure}%
|
|
~
|
|
\begin{subfigure}{0.5\textwidth}
|
|
\centering
|
|
\includegraphics[scale=0.2]{backup-app-pirho-struct.png}
|
|
\caption{Model $\pi\rho$,\\
|
|
merge African and English networks}
|
|
\end{subfigure}%
|
|
\caption{Structures detected for networks
|
|
of~\cite{baldockDailyTemporalStructure2011,
|
|
baldockSystemsApproachReveals2019}}
|
|
\end{figure}
|
|
\end{frame}
|
|
|
|
\begin{frame}{Clustering algorithm}
|
|
\centering
|
|
\vspace{0.25\baselineskip}
|
|
\begin{tikzpicture}[scale=0.85]
|
|
\input{tikz/clustering.tex}
|
|
\end{tikzpicture}
|
|
\[
|
|
D_{\mathcal{M}}(m,m') = \sum_{q = 1}^{Q_1} \sum_{r = 1}^{Q_2} \max(\widetilde{\pi}_{q}^{m}, \widetilde{\pi}_{q}^{m'}) \left( \widetilde{\alpha}_{qr}^{m} - \widetilde{\alpha}_{qr}^{m'}\right)^{2} \max(\widetilde{\rho}_{r}^{m}, \widetilde{\rho}_{r}^{m'})
|
|
\]
|
|
\end{frame}
|
|
|
|
\begin{frame}{Results}
|
|
\begin{figure}[ht]
|
|
\centering
|
|
\begin{subfigure}{0.5\textwidth}
|
|
\centering
|
|
\includegraphics[width=1\textwidth]{tikz/applications/baldock/bisbm-mat-Baldock2011_TB+Baldock2011_JN.pdf}
|
|
\caption{Reordered by LBM}
|
|
\end{subfigure}\hfil
|
|
\begin{subfigure}{0.5\textwidth}
|
|
\centering
|
|
\includegraphics[width=1\textwidth]{tikz/applications/baldock/pirho-colbisbm-mat-Baldock2011_TB+Baldock2011_JN.pdf}
|
|
\caption{Reordered by $\pi\rho$-colBiSBM}
|
|
\end{subfigure}
|
|
|
|
\caption{Reordered adjacency matrix by $\pi\rho$-colBiSBM,~\cite{baldockDailyTemporalStructure2011}}
|
|
\end{figure}
|
|
\end{frame}
|
|
|
|
\section{Conclusion}
|
|
\label{sec:conclusion}
|
|
\begin{frame}
|
|
\frametitle{Conclusion and perspectives}
|
|
% DONE Add a conclusion perspective slide
|
|
% Recall models with clustering
|
|
% Mention analysis of corrected networks for sampling
|
|
% Link to the package
|
|
\begin{block}{Capabilities}
|
|
\begin{itemize}
|
|
\item 4 models including 3 with flexibility on at least one of
|
|
the dimensions (adaptability to data).
|
|
\item Detect classic and less classic structures in an agnostic way.
|
|
\item Partition a set of networks according to their structures.
|
|
\end{itemize}
|
|
\end{block}
|
|
|
|
\end{frame}
|
|
|
|
\begin{frame}{Perspectives}
|
|
\begin{itemize}
|
|
\item Investigate stability against randomness and local \emph{optima}.
|
|
\item Proof of identifiability of the $\pi\rho$ model.
|
|
\end{itemize}
|
|
|
|
\begin{block}{Package and applications}
|
|
\begin{itemize}
|
|
\item Integration into the \texttt{colSBM} package, improvement of user interface and
|
|
addition of ecologists' feedback
|
|
\item CRAN publication
|
|
\item Integrate the possibility of an additional criterion for clustering
|
|
\item Apply clustering to data from
|
|
\cite{pichonTellingMutualisticAntagonistic2024,doreRelativeEffectsAnthropogenic2021}
|
|
\end{itemize}
|
|
|
|
\end{block}
|
|
\bigskip
|
|
\centering
|
|
Thank you for your attention~!
|
|
\end{frame} |