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{"title":"Bilan semaine 13 2025 : 17-21 mars","markdown":{"yaml":{"title":"Bilan semaine 13 2025 : 17-21 mars","date":"17 03 2025","categories":["colBiSBM"]},"containsRefs":false,"markdown":"\n\nCette semaine j'ai :\n\n- Fini d'intégrer à colSBM tous les changements (clustering dérecursifier pour \nuni et bipartites& cli ...) et contacter Saint-Clair pour passer colSBM sous GrossSBM.\n- Relancer et obtenus les résultats pour le clustering sur les réseaux Baldock\n\n\n\n\n\n\n\n\n\n- Relancer et obtenus les résultats pour les simus ajoutant du bruits sur les structures et liens\n - Pour *noisy $\\alpha$*:\n\n Plan de simulation 2 collections ($d\\in (1,2)$) avec $M = 30$ soit 15 réseaux par type. $n_r = n_c = 120$ et\n $$\\pi_1 = \\begin{pmatrix} 0.5, 0.3, 0.2\\end{pmatrix},~\n \\rho_1 = \\begin{pmatrix}0.4, 0.3, 0.2, 0.1\\end{pmatrix},~\n \\alpha_1 = \\begin{pmatrix}\n 0.85& 0.4& 0.2& 0.15\\\\\n 0.6& 0.2& 0.15& 0.15\\\\\n 0.2& 0.15& 0.15& 0.7\n \\end{pmatrix}$$\n\n $$ \\pi_2 = (0.5, 0.3, 0.2),~\n \\rho_2 = (0.45, 0.3, 0.25),~\n \\alpha_2 = \\begin{pmatrix}\n 0.65& 0.15& 0.15\\\\\n 0.15& 0.8& 0.15\\\\\n 0.15& 0.15& 0.4\n \\end{pmatrix}$$\n \n $\\epsilon \\in (0, 0.01, \\dots 0.05)$ qui est l'écart-type d'une $\\mathcal{N}_{Q_1^d \\times Q_2^d}(0,\\epsilon^2) = vec(N^m), \\forall m \\in (1,\\dots, M)$.\n Et $\\forall m, X^m \\sim LBM_{n_r,n_c}(Q_1^d, Q_2^d, \\alpha_d + N^m, \\pi_d, \\rho_d)$\n\n Résultats :\n \n \n - Pour *noisy links*:\n \n Plan de simu $M = 30$, $n_r = n_c = 120$.\n $$\\pi_1 = \\begin{pmatrix} 0.5, 0.3, 0.2\\end{pmatrix},~\n \\rho_1 = \\begin{pmatrix}0.4, 0.3, 0.2, 0.1\\end{pmatrix},~\n \\alpha_1 = \\begin{pmatrix}\n 0.85& 0.4& 0.2& 0.05\\\\\n 0.6& 0.2& 0.05& 0.05\\\\\n 0.2& 0.05& 0.05& 0.7\n \\end{pmatrix}$$\n\n $$ \\pi_2 = (0.5, 0.3, 0.2),~\n \\rho_2 = (0.45, 0.3, 0.25),~\n \\alpha_2 = \\begin{pmatrix}\n 0.65& 0.05& 0.05\\\\\n 0.05& 0.8& 0.05\\\\\n 0.05& 0.05& 0.4\n \\end{pmatrix}$$\n\n $\\epsilon \\in (0, 0.05, \\dots 0.5)$, indices de la matrice = sample.int($n_r \\times n_c$, size = $n_r \\times n_c \\times \\epsilon$). Les indices tirés inverse la valeur du lien (1 -> 0, 0 -> 1)\n \n\n::: {#fig-results-linsk layout-ncol=2}\n\n\n\n\n\n:::\n\n- Relancer simulations robustesse aux NAs\n\n- Changer les plots résultats NAs pour faire sous-plots comparant sep vs model.","srcMarkdownNoYaml":"\n\nCette semaine j'ai :\n\n- Fini d'intégrer à colSBM tous les changements (clustering dérecursifier pour \nuni et bipartites& cli ...) et contacter Saint-Clair pour passer colSBM sous GrossSBM.\n- Relancer et obtenus les résultats pour le clustering sur les réseaux Baldock\n\n\n\n\n\n\n\n\n\n- Relancer et obtenus les résultats pour les simus ajoutant du bruits sur les structures et liens\n - Pour *noisy $\\alpha$*:\n\n Plan de simulation 2 collections ($d\\in (1,2)$) avec $M = 30$ soit 15 réseaux par type. $n_r = n_c = 120$ et\n $$\\pi_1 = \\begin{pmatrix} 0.5, 0.3, 0.2\\end{pmatrix},~\n \\rho_1 = \\begin{pmatrix}0.4, 0.3, 0.2, 0.1\\end{pmatrix},~\n \\alpha_1 = \\begin{pmatrix}\n 0.85& 0.4& 0.2& 0.15\\\\\n 0.6& 0.2& 0.15& 0.15\\\\\n 0.2& 0.15& 0.15& 0.7\n \\end{pmatrix}$$\n\n $$ \\pi_2 = (0.5, 0.3, 0.2),~\n \\rho_2 = (0.45, 0.3, 0.25),~\n \\alpha_2 = \\begin{pmatrix}\n 0.65& 0.15& 0.15\\\\\n 0.15& 0.8& 0.15\\\\\n 0.15& 0.15& 0.4\n \\end{pmatrix}$$\n \n $\\epsilon \\in (0, 0.01, \\dots 0.05)$ qui est l'écart-type d'une $\\mathcal{N}_{Q_1^d \\times Q_2^d}(0,\\epsilon^2) = vec(N^m), \\forall m \\in (1,\\dots, M)$.\n Et $\\forall m, X^m \\sim LBM_{n_r,n_c}(Q_1^d, Q_2^d, \\alpha_d + N^m, \\pi_d, \\rho_d)$\n\n Résultats :\n \n \n - Pour *noisy links*:\n \n Plan de simu $M = 30$, $n_r = n_c = 120$.\n $$\\pi_1 = \\begin{pmatrix} 0.5, 0.3, 0.2\\end{pmatrix},~\n \\rho_1 = \\begin{pmatrix}0.4, 0.3, 0.2, 0.1\\end{pmatrix},~\n \\alpha_1 = \\begin{pmatrix}\n 0.85& 0.4& 0.2& 0.05\\\\\n 0.6& 0.2& 0.05& 0.05\\\\\n 0.2& 0.05& 0.05& 0.7\n \\end{pmatrix}$$\n\n $$ \\pi_2 = (0.5, 0.3, 0.2),~\n \\rho_2 = (0.45, 0.3, 0.25),~\n \\alpha_2 = \\begin{pmatrix}\n 0.65& 0.05& 0.05\\\\\n 0.05& 0.8& 0.05\\\\\n 0.05& 0.05& 0.4\n \\end{pmatrix}$$\n\n $\\epsilon \\in (0, 0.05, \\dots 0.5)$, indices de la matrice = sample.int($n_r \\times n_c$, size = $n_r \\times n_c \\times \\epsilon$). Les indices tirés inverse la valeur du lien (1 -> 0, 0 -> 1)\n \n\n::: {#fig-results-linsk layout-ncol=2}\n\n\n\n\n\n:::\n\n- Relancer simulations robustesse aux NAs\n\n- Changer les plots résultats NAs pour faire sous-plots comparant sep vs model."},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":"auto","echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"engine":"markdown"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":false,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":true,"html-math-method":"katex","embed-resources":true,"output-file":"2025-13.html"},"language":{"toc-title-document":"Table 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