• Aucun résultat trouvé

Model selection and clustering in stochastic block models with the exact integrated complete data likelihood

N/A
N/A
Protected

Academic year: 2021

Partager "Model selection and clustering in stochastic block models with the exact integrated complete data likelihood"

Copied!
33
0
0

Texte intégral

Figure

Figure 1: Mean of mutual information between estimated and true cluster membership matrices using 20 simulated graphs for each value of β in {0.45, 0.43,
Figure 2: Mean of mutual information between estimated and true cluster membership matrices using 20 simulated graphs for each value of β in {0.45, 0.43,
Figure 3: Mean of the mutual information between estimated and true cluster membership matrices using 20 simulated graphs with N = 10000 and K = 50.
Figure 4: Adjacency matrix of the network of blogs, the rows/columns are sorted by cluster number with clusters found by the greedy ICL algorithm
+2

Références

Documents relatifs

algorithms distance kernel solution let theorem clustering graph matrix points cluster means. algorithms distance kernel solution let theorem clustering graph matrix points

In particular, the consistency of these estimators is settled for the probability of an edge between two vertices (and for the group pro- portions at the price of an

Human immunodeficiency virus type 1 (HIV-1) RNA and p24 antigen concentrations were determined in plasma samples from 169 chronically infected patients (median CD4 cell count,

So in this thesis, to simplify the parameters estimation so thus make the binned-EM and bin-EM-CEM algorithms more efficient, and also to better adapt different data distribution

chapitre on a exposé les résultats obtenus (modèle d’étude et optimisations des conditions opératoires de la réaction de couplage, les produits synthétisés)

We tackle the community detection problem in the Stochastic Block Model (SBM) when the communities of the nodes of the graph are assigned with a Markovian dynamic.. To recover

data generating process is necessary for the processing of missing values, which requires identifying the type of missingness [Rubin, 1976]: Missing Completely At Random (MCAR)