Add to ORKGWe present a Bayesian nonparametric Poisson factorization model for modeling dense network data with an unknown and potentially growing number of overlapping communities. The construction is based on completely random measures and allows the number of communities to either increase with the number of nodes at a specified logarithmic or polynomial rate, or be bounded. We develop asymptotics for the number and size of the communities of the network and derive a Markov chain Monte Carlo algorithm for targeting the exact posterior distribution for this model. The usefulness of the approach is illustrated on various real networks
We propose two novel approaches for recommender systems and networks. In the first part, we first gi...
In this article, we introduce a dynamic generative model, the Bayesian allocation model (BAM), for m...
International audienceWe present a non parametric bayesian inference strategy to automatically infer...
We develop a novel Bayesian nonparametric model for random bipartite graphs. The model is based on t...
Bayesian Statistics provide us with a powerful approach to model real-world phenomena and quantify t...
This thesis is concerned with the statistical learning of probabilistic models for graph-structured ...
We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictio...
Replicated network data are increasingly available in many research fields. For example, in connecto...
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks...
A wide variety of natural or artificial systems can be modeled as time-varying or temporal networks....
We propose a novel statistical model for sparse networks with overlapping community structure. The m...
Collections of networks are available in many research fields. In connectomic applications, inter-co...
We propose a novel statistical model for sparse networks with overlapping community structure. The m...
<p>We present a probabilistic model for learning from dynamic relational data, wherein the observed ...
We propose a nonparametric framework for the analysis of networks, based on a natural limit object t...
We propose two novel approaches for recommender systems and networks. In the first part, we first gi...
In this article, we introduce a dynamic generative model, the Bayesian allocation model (BAM), for m...
International audienceWe present a non parametric bayesian inference strategy to automatically infer...
We develop a novel Bayesian nonparametric model for random bipartite graphs. The model is based on t...
Bayesian Statistics provide us with a powerful approach to model real-world phenomena and quantify t...
This thesis is concerned with the statistical learning of probabilistic models for graph-structured ...
We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictio...
Replicated network data are increasingly available in many research fields. For example, in connecto...
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks...
A wide variety of natural or artificial systems can be modeled as time-varying or temporal networks....
We propose a novel statistical model for sparse networks with overlapping community structure. The m...
Collections of networks are available in many research fields. In connectomic applications, inter-co...
We propose a novel statistical model for sparse networks with overlapping community structure. The m...
<p>We present a probabilistic model for learning from dynamic relational data, wherein the observed ...
We propose a nonparametric framework for the analysis of networks, based on a natural limit object t...
We propose two novel approaches for recommender systems and networks. In the first part, we first gi...
In this article, we introduce a dynamic generative model, the Bayesian allocation model (BAM), for m...
International audienceWe present a non parametric bayesian inference strategy to automatically infer...