Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Networks are used in many scientific fields such as biology, social sci- ence, and information technology. They aim at modelling, with edges, the way objects of interest, represented by vertices, are related to each other. Looking for clusters of vertices, also called communities or modules, has appeared to be a pow- erful approach for capturing the underlying structure of a network. In this context, the Block-Clustering model has been applied on random graphs. The principle of this method is to assume that given the latent structure of a graph, the edges are independent and generated from a parametric distribution. Many EM-like strategies have been proposed, in a frequentist setting, to optimize the parameters of the model. Moreover, a criterion, based on an asymptotic approximation of the Integrated Clas- sification Likelihood (ICL), has recently been derived to estimate the number of classes in the latent structure. ,  show how the Block-Clustering model can be described in a full Bayesian framework and how the posterior distri- bution, of all the parameters and latent variables, can be approximated efficiently applying Variational Bayes (VB).  also propose a new non-asymptotic Bayesian model selection criterion. Using simulated data sets,  compare our approach to other strategies.  show that our criterion can outperform ICL.
['Pierre Latouche', 'Etienne Birmelé', 'Christophe Ambroise']
I am writing a research paper on a idea Bayesian Methods for Graph Clustering
[{"from": "human", "value": "I am writing a research paper on a idea Bayesian Methods for Graph Clustering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Networks are used in many scientific fields such as biology, social sci- ence, and information technology. They aim at modelling, with edges, the way objects of interest, represented by vertices, are related to each other. Looking for clusters of vertices, also called communities or modules, has appeared to be a pow- erful approach for capturing the underlying structure of a network. In this context, the Block-Clustering model has been applied on random graphs. The principle of this method is to assume that given the latent structure of a graph, the edges are independent and generated from a parametric distribution. Many EM-like strategies have been proposed, in a frequentist setting, to optimize the parameters of the model. Moreover, a criterion, based on an asymptotic approximation of the Integrated Clas- sification Likelihood (ICL), has recently been derived to estimate the number of classes in the latent structure. ,  show how the Block-Clustering model can be described in a full Bayesian framework and how the posterior distri- bution, of all the parameters and latent variables, can be approximated efficiently applying Variational Bayes (VB).  also propose a new non-asymptotic Bayesian model selection criterion. Using simulated data sets,  compare our approach to other strategies.  show that our criterion can outperform ICL."}]