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tensor factorization With Sparse and Graph Regularization for Fake News Detection on Social Networks

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第4期11卷 4888-4898页

作者： Che, Hangjun Pan, Baicheng Leung, Man-Fai Cao, Yuting Yan, Zheng Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Anglia Ruskin Univ Fac Sci & Engn Cambridge CB1 1PT England Hamad Bin Khalifa Univ Coll Sci & Engn Doha Qatar Univ Technol Sydney Australian AI Inst Fac Engn & Informat Technol Ultimo NSW Australia

Social media has a significant influence, which greatly facilitates people to stay up-to-date with information. Unfortunately, a great deal of fake news on social media misleads people and causes a lot of losses. Therefore, fake news detection is necessary to address this issue. Recently, social content category-based methods have become a crucial component of fake news detection. Different from news context-based category, which focuses on word embedding, it tends to explore the potential relationships and structures between users and news. In this article, a third-order tensor, which obtains massive information and connections, is constructed by the social links and engagements of social networks. Then, a sparse and graph-regularized CANDECOMP/PARAFAC (SGCP) tensor decomposition learning method is proposed for fake news detection on social network. In SGCP, a news factor matrix is constructed by CP decomposition of the tensor, which reflects the complex connections among users and news. Furthermore, SGCP retains sparsity of the news factor matrix and preserves the manifold structures from the original space. In addition, an efficient optimization algorithm, which is proven to be monotonically nonincreasing, is proposed to solve SGCP. Finally, abundant experiments are conducted on real-world datasets and demonstrate the effectiveness of the proposed SGCP.

关键词： Fake news detection social media mining tensor factorization

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tensor factorization via transformed tensor-tensor product for image alignment

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NUMERICAL ALGORITHMS 2024年第3期95卷 1251-1289页

作者： Xia, Sijia Qiu, Duo Zhang, Xiongjun Cent China Normal Univ Sch Math & Stat Wuhan 430079 Peoples R China Wuhan Inst Technol Sch Math & Phys Wuhan 430205 Peoples R China Cent China Normal Univ Hubei Key Lab Math Sci Wuhan 430079 Peoples R China

In this paper, we study the problem of a batch of linearly correlated image alignment, where the observed images are deformed by some unknown domain transformations, and corrupted by additive Gaussian noise and sparse noise simultaneously. By stacking these images as the frontal slices of a third-order tensor, we propose to utilize the tensor factorization method via transformed tensor-tensor product to explore the low-rankness of the underlying tensor, which is factorized into the product of two smaller tensors via transformed tensor-tensor product under any unitary transformation. The main advantage of transformed tensor-tensor product is that its computational complexity is lower compared with the existing literature based on transformed tensor nuclear norm. Moreover, the tensor l(p) (0 < p < 1) norm is employed to characterize the sparsity of sparse noise and the tensor Frobenius norm is adopted to model additive Gaussian noise. A generalized Gauss-Newton algorithm is designed to solve the resulting model by linearizing the domain transformations, and a proximal Gauss-Seidel algorithm is developed to solve the corresponding subproblem. Furthermore, the convergence of the proximal Gauss-Seidel algorithm is established according to Kurdyka-Lojasiewicz property, whose convergence rate is also analyzed. Extensive numerical examples on real-world image datasets are carried out to demonstrate the superior performance of the proposed method as compared to several state-of-the-art methods in both accuracy and computational time.

关键词： Image alignment Transformed tensor-tensor product tensor factorization Proximal Gauss-Seidel algorithm

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tensor factorization-Based Method for tensor Completion with Spatio-temporal Characterization

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2023年第1期199卷 337-362页

作者： Yu, Quan Zhang, Xinzhen Huang, Zheng-Hai Hunan Univ Sch Math Changsha 410082 Hunan Peoples R China Tianjin Univ Sch Math Tianjin 300354 Peoples R China

In this paper, we propose a novel tensor factorization-based method for the third-order tensor completion problem with spatio-temporal characterization. For this aim, we consider tensor fibered rank, which extends tubal rank, to improve the flexibility and accuracy of data characterization. Based on this rank, we apply a factorization-based method to complete the third-order low-rank tensors with spatio-temporal characteristics, which are intrinsic features of image, video and internet traffic tensor data. The model not only makes good use of the low-rank structure of tensors, but also takes into account the spatio-temporal characteristics of the data. Finally, we report numerical results on completing image, video and internet traffic data. The results demonstrate that our method outperforms some existing methods.

关键词： tensor completion tensor factorization tensor fibered rank Spatio-temporal characteristics

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tensor factorization and Attention-Based CNN-LSTM Deep-Learning Architecture for Improved Classification of Missing Physiological Sensors Data

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IEEE SENSORS JOURNAL 2023年第2期23卷 1286-1294页

作者： Akmal, Muhammad Riphah Int Univ Dept Biomed Engn Lahore 55150 Pakistan

Oneof the essential issues for efficient control of prosthesis is the accurate classification of target movements hidden in electroencephalography (EEG) and electromyography (EMG) signals. However, in the presence of missing data in acquired signals, the classification accuracy degrades significantly as the amount of missing data increases, reducing the control performance of the prosthesis. This research proposes a framework based on tensor (multidimensional array) factorization and attention-based convolutional neural network (CNN)-long short-termmemory (LSTM) deep learning (DL) for recovering missing data and performing classification of target movements, respectively. To recover missing data in tensor factorization, Canonical/Polyadic Weighted OPTimization (CP-WOPT) is employed, and its performance is compared to state-of-the-art factorization methods, whereas the performance of CNN-LSTM-attention layer (Attn) is compared to state-of-the-art machine learning and DL classifiers. Results show that CNN-LSTM-Attn obtained the mean classification accuracy of 98%, 83%, and 90% on complete (0% missing data), partially complete (10% to 50% missing data), and tensor-recovered real-world EEG and EMG data, respectively, demonstrating the applicability of the proposed framework.

关键词： Attention mechanism classification convolutional neural network (CNN) deep learning (DL) long short-term memory (LSTM) missing data tensor factorization

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When non-local similarity meets tensor factorization: A patch-wise method for hyperspectral anomaly detection

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SIGNAL PROCESSING 2025年 230卷

作者： Meng, Lixiang Xu, Yanhui Shen, Qiangqiang Chen, Yongyong Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Nanjing 210023 Peoples R China Harbin Inst Technol Shenzhen Sch Elect & Informat Engn Shenzhen 518055 Peoples R China

Hyperspectral anomaly detection (HAD) entails identifying anomaly pixels in hyperspectral images (HSI) that significantly diverge from the background spectral signatures. However, the conventional tensor-based HAD methods typically leverage tensor nuclear norm (TNN) and its variants to exploit the low-rank characteristic on the image-level HSI. Specifically, the omission of non-local similarity features in HAD reduces the potential to leverage pertinent information, leading to the loss of crucial HSI structures and details. In addition, the tensor singular value decomposition (t-SVD) involved in solving the TNN minimization tends to augment the computational time as the data size expands. To address these issues, a novel patch-wise model for HAD that integrates non-local similarity with tensor factorization (NSTF) is proposed. Specifically, instead of using the entire image, we employ aggregated non-local similarity patches to characterize the low-rank structure of HSI, accounting for both spectral correlation and non-local similarity simultaneously and equivalently. Moreover, we introduce tensor factorization to factorize the large-scale tensor generated by grouping non-local similarity patches into two small-scale tensors, which more efficiently exploits its low-rank property of background. Finally, comprehensive experimental results validated on HSI datasets unequivocally establish the superiority of our proposed method over cutting-edge methods.

关键词： Hyperspectral anomaly detection Non-local similarity tensor factorization

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CTF-DDI: Constrained tensor factorization for drug-drug interactions prediction

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 161卷 26-34页

作者： Han, Guosheng Peng, Lingzhi Ding, Aocheng Zhang, Yan Lin, Xuan Xiangtan Univ Sch Math & Computat Sci Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Sch Comp Sci Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Hunan Key Lab Computat & Simulat Sci & Engn Xiangtan 411105 Hunan Peoples R China

Computational approaches for predicting drug-drug interactions (DDI) can significantly facilitate combination therapy and drug discovery. Existing similarity-based methods often overlook simple yet valuable structural information or ignore multiple relationships from biological entities (e.g., target proteins and enzymes). Meanwhile, matrix factorization-based methods can alleviate the inherent sparsity issues in DDI data. However, this line of work usually only considers the original association information of DDI pairs. To address these issues, we proposed a novel tensor factorization strategy with effective constraint terms (CTF-DDI) for potential DDI prediction. Specifically, we first obtained drug features by constructing specific similarity matrices based on drug structure and drug-related biological associations. Then, a novel constrained tensor factorization (CTF) module was designed to further reconstruct drug similarity by introducing Hessian and L 21 regularization as constraints. Finally, we trained a deep neural network to extract nonlinear features for DDI prediction. Experimental results on two benchmark datasets demonstrated that the proposed CTF-DDI model outperforms classical tensor factorization and deep learning models. Furthermore, ablation and case studies validated the performance of CTF-DDI in DDI prediction. The source code of CTF-DDI is available at https://***/angelfacedac/CTF_DDI.

关键词： tensor factorization Similarity fusion Drug-drug interactions prediction

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A Multi-Aspect Neural tensor factorization Framework for Patent Litigation Prediction

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IEEE TRANSACTIONS ON BIG DATA 2024年第1期10卷 35-54页

作者： Wu, Han Zhu, Guanqi Liu, Qi Zhu, Hengshu Wang, Hao Zhao, Hongke Liu, Chuanren Chen, Enhong Xiong, Hui Univ Sci & Technol China Sch Comp Sci Anhui Prov Key Lab Big Data Anal & Applicat State Key Lab Cognit Intelligence Hefei 230027 Peoples R China BOSS Zhipin Career Sci Lab Beijing 100028 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Dept Comp Sci & Engn Guangzhou 511458 Peoples R China Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China Univ Tennessee Business Analyt & Stat Dept Knoxville TN 37996 USA

Patent litigation is an expensive and time-consuming legal process. To reduce costs, companies can proactively manage patents using predictive analysis to identify potential plaintiffs, defendants, and patents that may lead to litigation. However, there has been limited progress in predicting patent litigation due to the scarcity of lawsuits, the complexities of intentions, and the diversity of litigation characteristics. To this end, in this paper, we summarize the major causes of patent litigation into multiple aspects: the complex relations among plaintiffs, defendants and patents as well as the diverse content information from them. Along this line, we propose a Multi-aspect Neural tensor factorization (MANTF) framework for patent litigation prediction. First, a Pair-wise tensor factorization (PTF) module is designed to capture the complex relations among plaintiffs, defendants and patents inherent in a three-dimensional tensor, which will produce factorized latent vectors for companies and patents with pair-wise ranking estimators. Then, to better represent the patents and companies as an aid for PTF, we design a Patent Embedding Network (PEN) module and a Mask Company Embedding Network (MCEN) module to generate content-aware embedding for them, where PEN represents patents based on their meta, textual and graphical features, and MCEN represents companies by integrating their intrinsic features and competitions. Next, to integrate these three modules together, we leverage a Gaussian prior on the difference between factorized representations and content-aware embedding, and train MANTF in an end-to-end way. In the end, final predictions for patent litigation, i.e., the potentially litigated plaintiffs, defendants and patents, can be made with the well-trained model. We conduct extensive experiments on two real-world datasets, whose results prove that MANTF not only helps predict potential patent litigation but also shows robustness under various data sparse situ

关键词： Patent litigation prediction tensor factorization data sparsity patent mining learning to rank

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Time-aware tensor factorization for temporal recommendation

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APPLIED INTELLIGENCE 2025年第1期55卷 1-16页

作者： Feng, Yali Wen, Wen Hao, Zhifeng Cai, Ruichu Guangdong Univ Technol Dept Comp Sci Guangzhou 510006 Peoples R China Shantou Univ Coll Sci Shantou 515063 Peoples R China

In recent years, temporal recommendation, which recommends items to users with considering temporal information has attracted widespread attention. How to capture and combine the time-varying user behavior distributions and the time-varying user behavior transition patterns is challenging. To address these challenges, we propose a Time-Aware tensor factorization for Temporal Recommendation (TATF4TRec). First, the personalized Markov transition tensors are applied to represent the users' temporal behaviors. Then a tensor factorization method is proposed to capture the time-varying patterns of these tensors. Furthermore, the model linearly combines the time-varying patterns of user behavior and predicts the recommended results at a given time. Extensive experiments on five datasets demonstrate that TATF4TRec outperforms the state-of-the-art baselines significantly.

关键词： Temporal recommendation Time-varying pattern tensor factorization

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Bayesian Dictionary Learning on Robust Tubal Transformed tensor factorization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第8期35卷 11091-11105页

作者： Luo, Qilun Li, Wen Xiao, Mingqing South China Normal Univ Sch Math Sci Guangzhou 510631 Peoples R China Southern Illinois Univ Carbondale Sch Math & Stat Sci Carbondale IL 62901 USA

The recent study on tensor singular value decomposition (t-SVD) that performs the Fourier transform on the tubes of a third-order tensor has gained promising performance on multidimensional data recovery problems. However, such a fixed transformation, e.g., discrete Fourier transform and discrete cosine transform, lacks being self-adapted to the change of different datasets, and thus, it is not flexible enough to exploit the low-rank and sparse property of the variety of multidimensional datasets. In this article, we consider a tube as an atom of a third-order tensor and construct a data-driven learning dictionary from the observed noisy data along the tubes of the given tensor. Then, a Bayesian dictionary learning (DL) model with tensor tubal transformed factorization, aiming to identify the underlying low-tubal-rank structure of the tensor effectively via the data-adaptive dictionary, is developed to solve the tensor robust principal component analysis (TRPCA) problem. With the defined pagewise tensor operators, a variational Bayesian DL algorithm is established and updates the posterior distributions instantaneously along the third dimension to solve the TPRCA. Extensive experiments on real-world applications, such as color image and hyperspectral image denoising and background/foreground separation problems, demonstrate both effectiveness and efficiency of the proposed approach in terms of various standard metrics.

关键词： tensors Bayes methods Electron tubes Dictionaries Sparse matrices Data models Matrix decomposition Bayesian dictionary learning (DL) tensor factorization tensor robust principal component problem tubal transform

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Graph-aware tensor factorization convolutional network for knowledge graph completion

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第5期15卷 1755-1766页

作者： Jin, Yuzhu Yang, Liu Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

Constructed by millions of triples, knowledge graph is a commonly used structured representation of information encoding both the entities of the real-world facts and their corresponding relations. Although knowledge graph contains rich information, it is often far from complete. Knowledge graph completion algorithms are able to fill in the missing information efficiently by inferring missing or correcting wrong facts based on existing ones. However, as a kind of graph structure data, the knowledge graph includes not only the attributes of entities and relationships but also the entire graph structure information. Graph convolutional networks can learn better embedding of entities and relationships by using the connectivity structure of graphs, which is conducive to the knowledge graph complement. Although the existing methods based on graph convolutional networks can simultaneously capture the graph structure information and the attributes of entities and relationships from the knowledge graph, there is still a lack of better information decoupling and representation methods. In this article, we propose a novel Graph-Aware tensor factorization Convolutional Network (GATFCN) for knowledge graph completion. GATFCN combines a graph convolutional network and a tucker decomposition as the encoder and decoder. It leverages the advantages of graph convolutional network and tensor decomposition in an end-to-end manner to enrich the representation of facts. For the encoder, the GCN model is used to utilize the local information of the graph structure and graph nodes. For the decoder, tensor factorization can decode the information encoded by GCN while enriching the data representation. According to the comprehensive numerical experiments performed, GATFCN outperforms previous state-of-the-art methods across a number of different benchmark datasets, illustrating its effectiveness in knowledge graph completion.

关键词： Knowledge graph completion Graph convolutional network tensor factorization

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