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Found 19 papers, 0 papers with code
Estimating mixed memberships in multi-layer networks
For real-world multi-layer networks with unknown community information, we introduce two novel modularity metrics to quantify the quality of mixed membership community detection.
Community detection by spectral methods in multi-layer networks
Moreover, our analysis indicates that spectral clustering with the debiased sum of squared adjacency matrices is generally superior to spectral clustering with the sum of adjacency matrices.
Latent class analysis by regularized spectral clustering
In this article, we propose two new algorithms to estimate a latent class model for categorical data.
Latent class analysis with weighted responses
To our knowledge, our WLCM is the first model for latent class analysis with weighted responses.
WGoM: A novel model for categorical data with weighted responses
This paper makes a valuable contribution to the literature by introducing a novel model that extends the applicability of the GoM model and provides a more flexible framework for analyzing categorical data with weighted responses.
Bipartite mixed membership distribution-free model. A novel model for community detection in overlapping bipartite weighted networks
To close this gap, we introduce a novel model, the Bipartite Mixed Membership Distribution-Free (BiMMDF) model.
Mixed membership distribution-free model
We also propose the fuzzy weighted modularity to evaluate the quality of community detection for overlapping weighted networks with positive and negative edge weights.
Distribution-Free Model for Community Detection
Community detection for unweighted networks has been widely studied in network analysis, but the case of weighted networks remains a challenge.
Overlapping and nonoverlapping models
In this paper, we propose an overlapping and nonoverlapping model to study directed networks in which row nodes have overlapping property while column nodes do not.
A useful criterion on studying consistent estimation in community detection
We summarize the idea of using separation condition for a standard network and sharp threshold of Erd\"os-R\'enyi random graph to study consistent estimation, compare theoretical error rates and requirements on network sparsity of spectral methods under models that can degenerate to stochastic block model as a four-step criterion SCSTC.
Community detection for weighted bipartite networks
Here, to model a weighted bipartite network, we introduce a Bipartite Distribution-Free model by releasing ScBM's distribution restriction.
Directed degree corrected mixed membership model and estimating community memberships in directed networks
By taking the advantage of DiMSC's equivalence algorithm which returns same estimations as DiMSC and the recent development on row-wise singular vector deviation, we show that the proposed algorithm is asymptotically consistent under mild conditions by providing error bounds for the inferred membership vectors of each row node and each column node under DiDCMM.
Directed mixed membership stochastic blockmodel
DiMMSB allows that row nodes and column nodes of the adjacency matrix can be different and these nodes may have distinct community structure in a directed network.
Estimating Mixed-Memberships Using the Symmetric Laplacian Inverse Matrix
Mixed membership community detection is a challenging problem.
Mixed-SCORE+ for mixed membership community detection
In the note Jin et al. (2018), the authors propose SCORE+ as an improvement of SCORE to handle with weak signal networks.
Consistency of regularized spectral clustering in degree-corrected mixed membership model
Here, under the degree-corrected mixed membership (DCMM) model, we propose an efficient approach called mixed regularized spectral clustering (Mixed-RSC for short) based on the regularized Laplacian matrix.
An improved spectral clustering method for community detection under the degree-corrected stochastic blockmodel
For community detection problem, spectral clustering is a widely used method for detecting clusters in networks.
Dual regularized Laplacian spectral clustering methods on community detection
Spectral clustering methods are widely used for detecting clusters in networks for community detection, while a small change on the graph Laplacian matrix could bring a dramatic improvement.
Community Detection by Principal Components Clustering Methods
Based on the classical Degree Corrected Stochastic Blockmodel (DCSBM) model for network community detection problem, we propose two novel approaches: principal component clustering (PCC) and normalized principal component clustering (NPCC).
