Regularized asymmetric nonnegative matrix factorization for clustering in directed networks

Tosyali, Ali; Kim, Jin-Ho; Choi, Jaepil; Jeong, Myong K.

doi:10.1016/j.patrec.2019.07.005

Cited by 19 publications

(4 citation statements)

References 24 publications

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“…In 2019, the FANMF method was introduced by Tosyali [ 33 ]. The main purpose of the FANMF method is to handle nonnegative data that have an asymmetric nature.…”

Section: Methodsmentioning

confidence: 99%

“…By incorporating the graph information, the recommendation accuracy is improved by leveraging the connectivity and relationships among users and items. In 2019, factorized asymmetric nonnegative matrix factorization (FANMF) was introduced by Tosyali et al by considering the asymmetric relationships between users and items [ 33 ]. It led to enhanced recommendation quality, a deeper understanding of user preferences, and ultimately provided personalized user experiences in various data analysis tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

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Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations

Tokala,

Enduri,

Lakshmi

et al. 2023

Entropy

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Matrix factorization is a long-established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial role in identifying groups and communities within intricate networks. To overcome the challenge of extensive computing resources with matrix factorization techniques, we present a novel framework that utilizes the inherent community information of the rating network. Our proposed approach, named Community-Based Matrix Factorization (CBMF), has the following steps: (1) Model the rating network as a complex bipartite network. (2) Divide the network into communities. (3) Extract the rating matrices pertaining only to those communities and apply MF on these matrices in parallel. (4) Merge the predicted rating matrices belonging to communities and evaluate the root mean square error (RMSE). In our experimentation, we use basic MF, SVD++, and FANMF for matrix factorization, and the Louvain algorithm is used for community division. The experimental evaluation on six datasets shows that the proposed CBMF enhances the quality of recommendations in each case. In the MovieLens 100K dataset, RMSE has been reduced to 0.21 from 1.26 using SVD++ by dividing the network into 25 communities. A similar reduction in RMSE is observed for the datasets of FilmTrust, Jester, Wikilens, Good Books, and Cell Phone.

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“…In 2019, the FANMF method was introduced by Tosyali [ 33 ]. The main purpose of the FANMF method is to handle nonnegative data that have an asymmetric nature.…”

Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations

Tokala,

Enduri,

Lakshmi

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…For example, Wang et al (2011) propose the asymmetric non-negative matrix factorization (Asymmetric NMF, ANMF), which decomposes the asymmetric adjacency matrix of the directed graph into non-negative matrix, and then divides the nodes into clusters based on the membership matrix. On this basis, Tosyali et al (2019) propose Regularized Asymmetric NMF (RANMF) and improve the accuracy and robustness of directed graph clustering. One of the most successful applications of NMF is in the field of computer vision.…”

Section: Non-negative Matrix Factorizationmentioning

confidence: 99%

Anomalous citations detection in academic networks

Liu,

Bai,

Wang

et al. 2024

Artif Intell Rev

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Citation network analysis attracts increasing attention from disciplines of complex network analysis and science of science. One big challenge in this regard is that there are unreasonable citations in citation networks, i.e., cited papers are not relevant to the citing paper. Existing research on citation analysis has primarily concentrated on the contents and ignored the complex relations between academic entities. In this paper, we propose a novel research topic, that is, how to detect anomalous citations. To be specific, we first define anomalous citations and propose a unified framework, named ACTION, to detect anomalous citations in a heterogeneous academic network. ACTION is established based on non-negative matrix factorization and network representation learning, which considers not only the relevance of citation contents but also the relationships among academic entities including journals, papers, and authors. To evaluate the performance of ACTION, we construct three anomalous citation datasets. Experimental results demonstrate the effectiveness of the proposed method. Detecting anomalous citations carry profound significance for academic fairness.

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“…These existing methods include the projected gradient method [34,52], the interior point method [37], the projected quasi-Newton method [22,51], the active-set method [3,46,24,31], the activeset-like method [27,28], the alternating nonnegative least squares based on block principal pivoting (ANLS-BPP) method [28]. There also exist many variants of NMF (1.1) that add constraints and/or penalty terms on U and V for better interpretation and representation of the characteristics of the tasks [1,48] including sparse NMF [9,44,23], orthogonal NMF [33], semi-NMF [42], Joint NMF [14], nonnegative tensor factorization [7,9,25,26,43], manifold NMF [49], kernel NMF [53], regularized NMF [44,47], Symmetric NMF [19,45], integer constrained [12], and so on.…”

mentioning

confidence: 99%

An Alternating Rank-K Nonnegative Least Squares Framework (ARkNLS) for Nonnegative Matrix Factorization

Chu¹,

Shi²,

Eswar³

et al. 2020

Preprint

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Nonnegative matrix factorization (NMF) is a prominent technique for data dimensionality reduction that has been widely used for text mining, computer vision, pattern discovery, and bioinformatics. In this paper, a framework called ARkNLS (Alternating Rank-k Nonnegativity constrained Least Squares) is proposed for computing NMF. First, a recursive formula for the solution of the rank-k nonnegativity-constrained least squares (NLS) is established. This recursive formula can be used to derive the closed-form solution for the Rank-k NLS problem for any integer k ≥ 1. As a result, each subproblem for an alternating rank-k nonnegative least squares framework can be obtained based on this closed form solution. Assuming that all matrices involved in rank-k NLS in the context of NMF computation are of full rank, two of the currently best NMF algorithms HALS (hierarchical alternating least squares) and ANLS-BPP (Alternating NLS based on Block Principal Pivoting) can be considered as special cases of ARkNLS with k = 1 and k = r for rank r NMF, respectively. This paper is then focused on the framework with k = 3, which leads to a new algorithm for NMF via the closed-form solution of the rank-3 NLS problem. Furthermore, a new strategy that efficiently overcomes the potential singularity problem in rank-3 NLS within the context of NMF computation is also presented. Extensive numerical comparisons using real and synthetic data sets demonstrate that the proposed algorithm provides state-of-the-art performance in terms of computational accuracy and cpu time.

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Regularized asymmetric nonnegative matrix factorization for clustering in directed networks

Cited by 19 publications

References 24 publications

Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations

Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations

Anomalous citations detection in academic networks

An Alternating Rank-K Nonnegative Least Squares Framework (ARkNLS) for Nonnegative Matrix Factorization

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