no code implementations • 5 Apr 2024 • Huan Qing
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.
no code implementations • 19 Mar 2024 • Huan Qing
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.
no code implementations • 28 Oct 2023 • Huan Qing
In this article, we propose two new algorithms to estimate a latent class model for categorical data.
no code implementations • 17 Oct 2023 • Huan Qing
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.
no code implementations • 17 Oct 2023 • Huan Qing
To our knowledge, our WLCM is the first model for latent class analysis with weighted responses.
no code implementations • 2 Nov 2022 • Huan Qing, Jingli Wang
To close this gap, we introduce a novel model, the Bipartite Mixed Membership Distribution-Free (BiMMDF) model.
no code implementations • 4 Dec 2021 • Huan Qing, Jingli Wang
We also propose the fuzzy weighted modularity to evaluate the quality of community detection for overlapping weighted networks with positive and negative edge weights.
no code implementations • 15 Nov 2021 • Huan Qing
Community detection for unweighted networks has been widely studied in network analysis, but the case of weighted networks remains a challenge.
no code implementations • 2 Nov 2021 • Huan Qing
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.
no code implementations • 30 Sep 2021 • Huan Qing
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.
no code implementations • 21 Sep 2021 • Huan Qing, Jingli Wang
Here, to model a weighted bipartite network, we introduce a Bipartite Distribution-Free model by releasing ScBM's distribution restriction.
no code implementations • 16 Sep 2021 • Huan Qing
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.
no code implementations • 7 Jan 2021 • Huan Qing, Jingli Wang
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.
no code implementations • 17 Dec 2020 • Huan Qing, Jingli Wang
Mixed membership community detection is a challenging problem.
no code implementations • 7 Dec 2020 • Huan Qing, Jingli Wang
In the note Jin et al. (2018), the authors propose SCORE+ as an improvement of SCORE to handle with weak signal networks.
no code implementations • 23 Nov 2020 • Huan Qing, Jingli Wang
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.
no code implementations • 12 Nov 2020 • Huan Qing, Jingli Wang
For community detection problem, spectral clustering is a widely used method for detecting clusters in networks.
no code implementations • 9 Nov 2020 • Huan Qing, Jingli Wang
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.
no code implementations • 9 Nov 2020 • Huan Qing, Jingli Wang
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).