Nonparametric Bayesian models of hierarchical structure in complex networks
Analyzing and understanding the structure of complex relational data is important in many applications including analysis of the connectivity in the human brain. Such networks can have prominent patterns on different scales, calling for a hierarchically structured model. We propose two non-parametric Bayesian hierarchical network models based on Gibbs fragmentation tree priors, and demonstrate their ability to capture nested patterns in simulated networks. On real networks we demonstrate detection of hierarchical structure and show predictive performance on par with the state of the art. We envision that our methods can be employed in exploratory analysis of large scale complex networks for example to model human brain connectivity.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach
This paper focuses on the problem of hierarchical non-overlapping clustering of a dataset. In such a clustering, each data item is associated with exactly one leaf node and each internal node is associated with all the d…
ClusteringA Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection
Bayesian nonparametric methods are naturally suited to the problem of out-of-distribution (OOD) detection. However, these techniques have largely been eschewed in favor of simpler methods based on distances between pre-t…
Out-of-Distribution DetectionOut of Distribution (OOD) DetectionNonparametric Variational Auto-encoders for Hierarchical Representation Learning
The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple…
ClusteringRepresentation LearningVariational InferenceCATVI: Conditional and Adaptively Truncated Variational Inference for Hierarchical Bayesian Nonparametric Models
Current variational inference methods for hierarchical Bayesian nonparametric models can neither characterize the correlation structure among latent variables due to the mean-field setting, nor infer the true posterior d…
ClusteringTopic ModelsVariational InferenceNonparametric Bayesian Topic Modelling with the Hierarchical Pitman-Yor Processes
The Dirichlet process and its extension, the Pitman-Yor process, are stochastic processes that take probability distributions as a parameter. These processes can be stacked up to form a hierarchical nonparametric Bayesia…
Topic Models