Stochastic Block Model
87 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
Scalable detection of statistically significant communities and hierarchies, using message-passing for modularity
We address this problem by using the modularity as a Hamiltonian at finite temperature, and using an efficient Belief Propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it.
On semidefinite relaxations for the block model
We put ours and previously proposed SDPs in a unified framework, as relaxations of the MLE over various sub-classes of the SBM, revealing a connection to sparse PCA.
MMSE of probabilistic low-rank matrix estimation: Universality with respect to the output channel
This paper considers probabilistic estimation of a low-rank matrix from non-linear element-wise measurements of its elements.
Nonparametric Bayesian inference of the microcanonical stochastic block model
A very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges, but also with an unlimited number of modules.
Semiparametric spectral modeling of the Drosophila connectome
We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome.
A network approach to topic models
By adapting existing community-detection methods -- using a stochastic block model (SBM) with non-parametric priors -- we obtain a more versatile and principled framework for topic modeling (e. g., it automatically detects the number of topics and hierarchically clusters both the words and documents).
A Bayesian Method for Joint Clustering of Vectorial Data and Network Data
We present a new model-based integrative method for clustering objects given both vectorial data, which describes the feature of each object, and network data, which indicates the similarity of connected objects.
Evaluating Overfit and Underfit in Models of Network Community Structure
These results introduce both a theoretically principled approach to evaluate over and underfitting in models of network community structure and a realistic benchmark by which new methods may be evaluated and compared.
The Power Mean Laplacian for Multilayer Graph Clustering
Multilayer graphs encode different kind of interactions between the same set of entities.
Multi-view Banded Spectral Clustering with Application to ICD9 Clustering
To bridge this gap, in this paper we propose a novel spectral clustering method that optimally combines multiple data sources while leveraging the prior distance knowledge.
