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Community Detection in Fully-Connected Multi-layer Networks through Joint Nonnegative Matrix Factorization

Esraa M. Al-sharoa, Selin Aviyente
2022 IEEE Access  
In this paper, a joint nonnegative matrix factorization approach is proposed to detect the community structure in multi-layer networks.  ...  The proposed approach models the multi-layer network as the union of a multiplex and bipartite network and formulates community detection as a regularized optimization problem.  ...  ACKNOWLEDGMENT The authors would like to thank to their colleague, Abdullah Karaaslanli, for sharing the multilayer Girvan Neman benchmark generation code.  ... 
doi:10.1109/access.2022.3168659 fatcat:sowzguhhxrhnlieyulfqb3l2ii

Second-order Symmetric Non-negative Latent Factor Analysis [article]

Weiling Li, Xin Luo
2022 arXiv   pre-print
Aiming at addressing this issue, this study proposes to incorporate an efficient second-order method into SNLF, thereby establishing a second-order symmetric non-negative latent factor analysis model for  ...  On the other hand, higher-order learning algorithms are expected to make a breakthrough, but their computation efficiency are greatly limited due to the direct manipulation of the Hessian matrix, which  ...  matrix factorization-based community detection models and their with the improved second-order methods directly.  ... 
arXiv:2203.02088v1 fatcat:qmrkbtn6zzhctnkpguka5belv4

Community discovery using nonnegative matrix factorization

Fei Wang, Tao Li, Xin Wang, Shenghuo Zhu, Chris Ding
2010 Data mining and knowledge discovery  
We choose Nonnegative Matrix Factorization (NMF) as our tool to find the communities because of its powerful interpretability and close relationship between clustering methods.  ...  In this paper, we will investigate another important issue, community discovery, in network analysis.  ...  Acknowledgment The work is partially supported by NSF grants IIS-0546280, CCF-0830659, and DMS-0915110.  ... 
doi:10.1007/s10618-010-0181-y fatcat:o3i3ekwrkjgdxi4hrsduirs3cy

Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization [article]

Meiby Ortiz-Bouza, Selin Aviyente
2022 arXiv   pre-print
The proposed algorithm employs Orthogonal Nonnegative Matrix Tri-Factorization to model each layer's adjacency matrix as the sum of two low-rank matrix factorizations, corresponding to the common and private  ...  Networks provide a powerful tool to model complex systems where the different entities in the system are presented by nodes and their interactions by edges.  ...  Section 3 provides background on community detection, multiplex networks, and orthogonal nonnegative matrix tri-factorization.  ... 
arXiv:2205.00626v1 fatcat:pyg22moqyffc7cj3vo3ckkmwyi

An Overlapping Community Detection Approach in Ego-Splitting Networks Using Symmetric Nonnegative Matrix Factorization

Mingqing Huang, Qingshan Jiang, Qiang Qu, Abdur Rasool
2021 Symmetry  
to supplement symmetric adjacent matrix, and finally identifies precise communities via nonnegative matrix factorization in each sub-network.  ...  To address this issue, we develop an overlapping community detection approach in Ego-Splitting networks using symmetric Nonnegative Matrix Factorization (ESNMF).  ...  Conclusions and Future Work A novel proposal is detailed in this paper for overlapping community detection in large-scale networks using symmetric nonnegative matrix factorization, which first divides  ... 
doi:10.3390/sym13050869 fatcat:bd64dzirtvdrliosag2fmatbri

Robust Community Detection in Graphs

Esraa M. Al-Sharoa, Bara' M. Ababneh, Mahmood A. Al-Khassaweneh
2021 IEEE Access  
The proposed approach combines robust principal component analysis (RPCA) and symmetric nonnegative matrix factorization (SymNMF) in a single optimization problem.  ...  In particular, the corrupted adjacency matrix is decomposed into a low-rank and sparse components using RPCA and the community structure is detected by applying SymNMF to the extracted low-rank component  ...  the symmetric nonnegative matrix factorization term.  ... 
doi:10.1109/access.2021.3105692 fatcat:qwfztffudzft7exrqehyhczl7u

Guest editorial: special issue on data mining with matrices, graphs and tensors

Tao Li, Chris Ding, Fei Wang
2011 Data mining and knowledge discovery  
Prominent examples include spectral clustering, non-negative matrix factorization, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) related clustering and dimension reduction,  ...  In these methods, the data is described using matrix representations (graphs are represented by their adjacency matrices) and the data mining problem is formulated as an optimization problem with matrix  ...  We would also like to thank all the authors who submitted their papers to the special issue. Special thanks to Dr. Geoff Webb for his great help and support in organizing the issue.  ... 
doi:10.1007/s10618-011-0214-1 fatcat:n4mf2ceoqzeitjdwq2opwtlm6e

An Interpretable Graph Generative Model with Heterophily [article]

Sudhanshu Chanpuriya, Ryan A. Rossi, Anup Rao, Tung Mai, Nedim Lipka, Zhao Song, Cameron Musco
2021 arXiv   pre-print
of communities, and c) optimizes effectively on real-world graphs with gradient descent on a cross-entropy loss.  ...  We propose the first edge-independent graph generative model that is a) expressive enough to capture heterophily, b) produces nonnegative embeddings, which allow link predictions to be interpreted in terms  ...  Note that entries of the factors X and Y are not necessarily nonnegative; hence this model does not directly admit an interpretation as community detection.  ... 
arXiv:2111.03030v1 fatcat:sc64kdqmnzdtvabhtajw6wqkum

Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs

Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama
2021 Neural Computation  
In this letter, we present a model, based on minimizing reconstruction error with nonnegative constraints, which relates to a Max-Cut criterion that simultaneously identifies the compressed nodes and the  ...  We further provide theoretical results on the identifiability of the model and the convergence of the proposed algorithms.  ...  This NECO_a_01402-Xu Theoretical Analysis In this section, we present theoretical results for the identification of nonnegative models and analysis of structured nonnegative matrix factorization (StNMF  ... 
doi:10.1162/neco_a_01402 fatcat:xgztzd7sbzcmhnr2bczlqbb7zu

Nonnegative Matrix Factorization: Models, Algorithms and Applications [chapter]

Zhong-Yuan Zhang
2012 Intelligent Systems Reference Library  
In recent years, Nonnegative Matrix Factorization (NMF) has become a popular model in data mining society.  ...  This chapter surveys NMF in terms of the model formulation and its variations and extensions, algorithms and applications, as well as its relations with K-means and Probabilistic Latent Semantic Indexing  ...  Jie Tang (Department of Computer Science and Technology, Tsinghua University, P.R.China) and Dr. Yong Wang (Academy of Mathematics and Systems Science, Chinese Academy of Sciences, P.R.China).  ... 
doi:10.1007/978-3-642-23241-1_6 fatcat:ohxfkn7wojhcrnkrshz2s7zxpu

Uncovering Community Structures with Initialized Bayesian Nonnegative Matrix Factorization

Xianchao Tang, Tao Xu, Xia Feng, Guoqing Yang, Peter Csermely
2014 PLoS ONE  
In view of the problems, a novel approach that utilizes an initialized Bayesian nonnegative matrix factorization model for determining community membership is proposed.  ...  Currently, several nonnegative matrix factorization algorithms have been proposed for discovering community structure in complex networks.  ...  Ioannis Psorakis for providing us with the Matlab code of their algorithm and Li Qiannan, Lu Min and Zuo Haichao for interesting discussions and useful advice. Author Contributions  ... 
doi:10.1371/journal.pone.0107884 pmid:25268494 pmcid:PMC4182427 fatcat:hmxhehmmhzflpdnpa5pcxabyom

Semi-supervised Community Detection via Constraint Matrix Construction and Active Node Selection

Suqi Zhang, Junyan Wu, Jianxin Li, Junhua Gu, Xianchao Tang, Xinyun Xu
2019 IEEE Access  
INDEX TERMS Community detection, non-negative matrix factorization, semi-supervised learning, active learning.  ...  Here, we present a novel semi-supervised and active learning method for community detection to integrate these two types of information of a network so as to increase the accuracy of community identification  ...  This is a popular method based on symmetric nonnegative matrix factorization for community detection. It is also an unsupervised method, which only takes topology structures into account.  ... 
doi:10.1109/access.2019.2962634 fatcat:pl2ba7l5qra5zbps4jfsq3jpfi

Extending a configuration model to find communities in complex networks

Di Jin, Dongxiao He, Qinghua Hu, Carlos Baquero, Bo Yang
2013 Journal of Statistical Mechanics: Theory and Experiment  
The experimental results demonstrate the superior performance of our method over competing methods in detecting both disjoint and overlapping communities.  ...  Extending a configuration model to find communities in complex networks determine the number of communities by applying consensus clustering.  ...  To be specific, Wang et al [9] used the squared loss and introduced an algorithm of symmetric nonnegative matrix factorization (SNMF) to minimize their loss function.  ... 
doi:10.1088/1742-5468/2013/09/p09013 fatcat:uj5hl2fydrc5fikazaqiy4dh6i

Topic Diffusion Discovery based on Sparseness-constrained Non-negative Matrix Factorization [article]

Yihuang Kang, Keng-Pei Lin, I-Ling Cheng
2018 arXiv   pre-print
In this paper, we consider a novel topic diffusion discovery technique that incorporates sparseness-constrained Non-negative Matrix Factorization with generalized Jensen-Shannon divergence to help understand  ...  Due to recent explosion of text data, researchers have been overwhelmed by ever-increasing volume of articles produced by different research communities.  ...  In this paper, we propose using a normalized Nonsmooth Nonnegative Matrix Factorization (nsNMF) [20] , which is originally defined as: X ≈WSH where S∈ℝ k ×k is a positive symmetric smoothing matrix defined  ... 
arXiv:1807.04386v1 fatcat:gj4ynkg4tncopcyl54t7rzoznm

Proximity Preserving Nonnegative Matrix Factorization

Yuya Ogawa, Koh Takeuchi, Yuya Sasaki, Makoto Onizuka
2020 Journal of Information Processing  
We present PPNMF, proximity preserving nonnegative matrix factorization for community detection.  ...  Although network embedding and representation learning methods are recently getting popular, we claim that they fall into suboptimal solutions for community detection, because they are based on indirect  ...  Proximity Preserving Nonnegative Matrix Factorization for Community Detection In this section, we present our method, PPNMF (Proximity Preserving Nonnegative Matrix Factorization).  ... 
doi:10.2197/ipsjjip.28.445 fatcat:v4jb2blrizhmxfpcf57qjll36y
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