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Found 6 papers, 1 papers with code
A Bayesian Nonparametric Latent Space Approach to Modeling Evolving Communities in Dynamic Networks
Our proposed approach, the hierarchical Dirichlet process latent position clustering model (HDP-LPCM), incorporates transitivity, models both individual and group level aspects of the data, and avoids the computationally expensive selection of the number of groups required by most popular methods.
Methodology
Higher-Order Spectral Clustering under Superimposed Stochastic Block Model
In particular, we prove non-asymptotic upper bounds on the misclustering error of spectral community detection for a SupSBM setting in which triangles or 3-uniform hyperedges are superimposed with undirected edges.
Spectral and matrix factorization methods for consistent community detection in multi-layer networks
We consider the problem of estimating a consensus community structure by combining information from multiple layers of a multi-layer network using methods based on the spectral clustering or a low-rank matrix factorization.
Null Models and Community Detection in Multi-Layer Networks
Multi-layer networks are networks on a set of entities (nodes) with multiple types of relations (edges) among them where each type of relation/interaction is represented as a network layer.
Community Detection
Methodology
Social and Information Networks
Physics and Society
Orthogonal symmetric non-negative matrix factorization under the stochastic block model
We present a method based on the orthogonal symmetric non-negative matrix tri-factorization of the normalized Laplacian matrix for community detection in complex networks.
Community detection in multi-relational data with restricted multi-layer stochastic blockmodel
We derive consistency results for community assignments of the maximum likelihood estimators (MLEs) in both models where MLSBM is assumed to be the true model, and either the number of nodes or the number of types of edges or both grow.
