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Interpretable variational graph autoencoder with Noninformative Prior

FUTURE INTERNET 2021年第2期13卷 51-51页

作者： Sun, Lili Liu, Xueyan Zhao, Min Yang, Bo Jilin Univ Coll Software Changchun 130012 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engineer Changchun 130012 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data. However, most existing methods based on variational (graph) autoencoder assume that the prior of latent variables obeys the standard normal distribution which encourages all nodes to gather around 0. That leads to the inability to fully utilize the latent space. Therefore, it becomes a challenge on how to choose a suitable prior without incorporating additional expert knowledge. Given this, we propose a novel noninformative prior-based interpretable variational graph autoencoder (NPIVGAE). Specifically, we exploit the noninformative prior as the prior distribution of latent variables. This prior enables the posterior distribution parameters to be almost learned from the sample data. Furthermore, we regard each dimension of a latent variable as the probability that the node belongs to each block, thereby improving the interpretability of the model. The correlation within and between blocks is described by a block-block correlation matrix. We compare our model with state-of-the-art methods on three real datasets, verifying its effectiveness and superiority.

关键词： neural networks network representation learning noninformative prior distribution variational graph autoencoder deep learning

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VGAE-CCI: variational graph autoencoder-based construction of 3D spatial cell-cell communication network

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BRIEFINGS IN BIOINFORMATICS 2024年第1期26卷 bbae619页

作者： Zhang, Tianjiao Zhang, Xiang Wu, Zhenao Ren, Jixiang Zhao, Zhongqian Zhang, Hongfei Wang, Guohua Wang, Tao Northeast Forestry Univ Coll Comp & Control Engn Harbin 150040 Peoples R China Harbin Inst Technol Fac Comp Harbin 150001 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China

Cell-cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation,and controlling immune responses. The rapid development of single-cell RNA sequencing and spatial transcriptomics sequencing(ST-seq) technologies provides essential data support for in-depth and comprehensive analysis of cell-cell communication. However,ST-seq data often contain incomplete data and systematic biases, which may reduce the accuracy and reliability of predicting cell-cell communication. Furthermore, other methods for analyzing cell-cell communication mainly focus on individual tissue sections,neglecting cell-cell communication across multiple tissue layers, and fail to comprehensively elucidate cell-cell communicationnetworks within three-dimensional tissues. To address the aforementioned issues, we propose VGAE-CCI, a deep learning frameworkbased on the variational graph autoencoder, capable of identifying cell-cell communication across multiple tissue layers. Additionally,this model can be applied to spatial transcriptomics data with missing or partially incomplete data and can clustered cells at single-cell resolution based on spatial encoding information within complex tissues, thereby enabling more accurate inference of cell-cellcommunication. Finally, we tested our method on six datasets and compared it with other state of art methods for predicting cell-cell communication. Our method outperformed other methods across multiple metrics, demonstrating its efficiency and reliability inpredicting cell-cell communication.

关键词： cell-cell communication scRNA-seq ST-seq variational graph autoencoder

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Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance

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BMC BIOLOGY 2023年第1期21卷 294-294页

作者： Zhu, Huan Hao, Hongxia Yu, Liang Xidian Univ Sch Comp Sci & Technol Xian Peoples R China

BackgroundEnormous clinical and biomedical researches have demonstrated that microbes are crucial to human health. Identifying associations between microbes and diseases can not only reveal potential disease mechanisms, but also facilitate early diagnosis and promote precision medicine. Due to the data perturbation and unsatisfactory latent representation, there is a significant room for *** this work, we proposed a novel framework, Multi-scale variational graph autoencoder embedding Wasserstein distance (MVGAEW) to predict disease-related microbes, which had the ability to resist data perturbation and effectively generate latent representations for both microbes and diseases from the perspective of distribution. First, we calculated multiple similarities and integrated them through similarity network confusion. Subsequently, we obtained node latent representations by improved variational graph autoencoder. Ultimately, XGBoost classifier was employed to predict potential disease-related microbes. We also introduced multi-order node embedding reconstruction to enhance the representation capacity. We also performed ablation studies to evaluate the contribution of each section of our model. Moreover, we conducted experiments on common drugs and case studies, including Alzheimer's disease, Crohn's disease, and colorectal neoplasms, to validate the effectiveness of our ***, our model exceeded other currently state-of-the-art methods, exhibiting a great improvement on the HMDAD database.

关键词： variational graph autoencoder Wasserstein distance Microbe-disease association XGBoost

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A variational graph autoencoder Aided Canonical Correlation Analysis Based Online Abnormal Patterns Detection Method for Buildings HVAC Systems

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IEEE Transactions on Consumer Electronics 2024年

作者： Pan, Xiaogang Deng, Qiao Jiao, Yuanyuan Chen, Zhiwen National University of Defense Technology College of System Engineering Changsha410073 China Central South University School of Automation Changsha410073 China

Heating, Ventilation, and Air Conditioning (HVAC) systems have become essential components of contemporary life, extensively employed in commercial and residential settings to ensure environmental comfort. However, worn components and suboptimal settings affect their operation, causing them to operate in abnormal conditions and leading to excessive energy consumption. The operating status of HVAC also fluctuates as load demand fluctuates, further complicating accurate anomaly detection. The advent of the Internet of Things technology has elevated the intelligence of HVAC systems, concurrently providing a robust data foundation for implementing data-driven methods in anomaly detection. Nonetheless, traditional data-driven detection methods often produce high false alarm rates when the system's operating modes change. This paper proposes a variational graph autoencoder aided canonical correlation analysis (CCA) based online abnormal patterns detection method. The main feature of this method is to analyze the correlation of historical data, thereby building a reconstruction graph that identifies and selects the most relevant neighboring nodes in the graph for the local modeling of current query data. On this basis, CCA is used for online anomaly detection. This method solves the problems of unclear cluster boundaries, difficulty determining the number of cluster centers, and more false alarms when working conditions change in traditional methods. Experimental comparative analysis using HVAC system data from actual buildings proves this method is superior to existing solutions regarding anomaly detection accuracy, false alarm rate, and false negative rate. © 1975-2011 IEEE.

关键词： abnormal patterns detection canonical correlation analysis HVAC systems Internet of Things variational graph autoencoder

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HPOseq: a deep ensemble model for predicting the protein-phenotype relationships based on protein sequences

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BMC BIOINFORMATICS 2025年第1期26卷 1-15页

作者： Zhao, Kai Ji, Zhuocheng Zhang, Linlin Quan, Na Li, Yuheng Yu, Guanglei Bi, Xuehua Xinjiang Univ Sch Comp Sci & Technol Urumqi 830011 Peoples R China Xinjiang Univ Sch Software Urumqi 830011 Peoples R China Xinjiang Med Univ Coll Med Engn & Technol Urumqi 830011 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China

BackgroundUnderstanding the relationships between proteins and specific disease phenotypes contributes to the early detection of diseases and advances the development of personalized medicine. The acquisition of a large amount of proteomics data has facilitated this process. To improve discovery efficiency and reduce the time and financial costs associated with biological experiments, various computational methods have yielded promising results. However, the lack of rich and reliable protein-related information still presents challenges in this *** this paper, we propose an ensemble prediction model, named HPOseq, which predicts human protein-phenotype relationships based only on sequence information. HPOseq establishes two base models to achieve objectives. One directly extracts internal information from amino acid sequences as protein features to predict the associated phenotypes. The other builds a protein-protein network based on sequence similarity, extracting information between proteins for phenotype prediction. Ultimately, an ensemble module is employed to integrate the predictions from both base models, resulting in the final *** results of 5-fold cross-validation reveal that HPOseq outperforms seven baseline methods for predicting protein-phenotype relationships. Moreover, we conduct case studies from the points of phenotype annotation and protein analysis to verify the practical significance of HPOseq.

关键词： Ensemble model variational graph autoencoder Deep learning Amino acid sequence

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Designing Connectivity-Guaranteed Porous Metamaterial Units Using Generative graph Neural Networks

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JOURNAL OF MECHANICAL DESIGN 2025年第2期147卷 021706页

作者： Wang, Zihan Bray, Austin Khanghah, Kiarash Naghavi Xu, Hongyi Univ Connecticut Sch Mech Aerosp & Mfg Engn Storrs CT 06269 USA

Designing 3D porous metamaterial units while ensuring complete connectivity of both solid and pore phases presents a significant challenge. This complete connectivity is crucial for manufacturability and structure-fluid interaction applications (e.g., fluid-filled lattices). In this study, we propose a generative graph neural network-based framework for designing the porous metamaterial units with the constraint of complete connectivity. First, we propose a graph-based metamaterial unit generation approach to generate porous metamaterial samples with complete connectivity in both solid and pore phases. Second, we establish and evaluate three distinct variational graph autoencoder (VGAE)-based generative models to assess their effectiveness in generating an accurate latent space representation of metamaterial structures. By choosing the model with the highest reconstruction accuracy, the property-driven design search is conducted to obtain novel metamaterial unit designs with the targeted properties. A case study on designing liquid-filled metamaterials for thermal conductivity properties is carried out. The effectiveness of the proposed graph neural network-based design framework is evaluated by comparing the performances of the obtained designs with those of known designs in the metamaterial database. Merits and shortcomings of the proposed framework are also discussed.

关键词： porous metamaterial graph neural network variational graph autoencoder design optimization generative design connectivity data-driven design design of engineered materials system machine learning

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Analysis of China's High-Speed Railway Network Using Complex Network Theory and graph Convolutional Networks

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BIG DATA AND COGNITIVE COMPUTING 2025年第4期9卷 101-101页

作者： Xu, Zhenguo Li, Jun Moulitsas, Irene Niu, Fangqu Cranfield Univ Ctr Computat Engn Sci Cranfield MK43 0AL Beds England Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China

This study investigated the characteristics and functionalities of China's High-Speed Railway (HSR) network based on Complex Network Theory (CNT) and graph Convolutional Networks (GCN). First, complex network analysis was applied to provide insights into the network's fundamental characteristics, such as small-world properties, efficiency, and robustness. Then, this research developed three novel GCN models to identify key nodes, detect community structures, and predict new links. Findings from the complex network analysis revealed that China's HSR network exhibits a typical small-world property, with a degree distribution that follows a log-normal pattern rather than a power law. The global efficiency indicator suggested that stations are typically connected through direct routes, while the local efficiency indicator showed that the network performs effectively within local areas. The robustness study indicated that the network can quickly lose connectivity if key nodes fail, though it showed an ability initially to self-regulate and has partially restored its structure after disruption. The GCN model for key node identification revealed that the key nodes in the network were predominantly located in economically significant and densely populated cities, positively contributing to the network's overall efficiency and robustness. The community structures identified by the integrated GCN model highlight the economic and social connections between official urban clusters and the communities. Results from the link prediction model suggest the necessity of improving the long-distance connectivity across regions. Future work will explore the network's socio-economic dynamics and refine and generalise the GCN models.

关键词： key node identification community detection link prediction small-world network graph attention networks variational graph autoencoder network efficiency and robustness

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Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation

Latent Space Score-based Diffusion Model for Probabilistic M...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liang, Guojun Abiri, Najmeh Hashemi, Atiye Sadat Lundström, Jens Byttner, Stefan Tiwari, Prayag School of Information Technology Halmstad University Halmstad Sweden Lund University Sweden

ISBN: (纸本)9798350368741

Accurate imputation is essential for the reliability and success of downstream tasks. Recently, diffusion models have attracted great attention in this field. However, these models neglect the latent distribution in a lower-dimensional space derived from the observed data, which limits the generative capacity of the diffusion model. Additionally, dealing with the original missing data without labels becomes particularly problematic. To address these issues, we propose the Latent Space Score-Based Diffusion Model (LSSDM) for probabilistic multivariate time series imputation. Observed values are projected onto low-dimensional latent space and coarse values of the missing data are reconstructed without knowing their ground truth values by this unsupervised learning approach. Finally, the reconstructed values are fed into a conditional diffusion model to obtain the precise imputed values of the time series. In this way, LSSDM not only possesses the power to identify the latent distribution but also seamlessly integrates the diffusion model to obtain the high-fidelity imputed values and assess the uncertainty of the dataset. Experimental results demonstrate that LSSDM achieves superior imputation performance while also providing a better explanation and uncertainty analysis of the imputation mechanism. The website of the code is https://***/gorgen2020/LSSDMimputation. © 2025 IEEE.

关键词： Diffusion model imputation multivariate time series variational graph autoencoder

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Cross-domain recommendation via knowledge distillation

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KNOWLEDGE-BASED SYSTEMS 2025年 311卷

作者： Li, Xiuze Huang, Zhenhua Wu, Zhengyang Wang, Changdong Chen, Yunwen South China Normal Univ Sch Artificial Intelligence Foshan 528225 Guangdong Peoples R China South China Normal Univ Sch Comp Sci Guangzhou 510631 Guangdong Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China DataGrandInc Res & Dev Dept Shanghai 201203 Peoples R China

Recommendation systems frequently suffer from data sparsity, resulting in less-than-ideal recommendations. A prominent solution to this problem is Cross-Domain Recommendation (CDR), which employs data from various domains to mitigate data sparsity and cold-start issues. Nevertheless, current mainstream methods, like feature mapping and co-training exploring domain relationships, overlook latent user-user and user-item similarities in the shared user-item interaction graph. Spurred by these deficiencies, this paper introduces KDCDR, a novel cross-domain recommendation framework that relies on knowledge distillation to utilize the data from the graph. KDCDR aims to improve the recommendation performance in both domains by efficiently utilizing information from the shared interaction graph. Furthermore, we enhance the effectiveness of user and item representations by exploring the relationships between user-user similarity and item-item similarity, as well as user-item interactions. The developed scheme utilizes the inner-domain graph as a teacher and the cross-domain graph as a student, where the student learns by distilling knowledge from the two teachers after undergoing a high-temperature distillation process. Furthermore, we introduce dynamic weight that regulates the learning process to prevent the student network from overly favoring learning from one domain and focusing on learning knowledge that the teachers have taught incorrectly. Through extensive experiments on four real-world datasets, KDCDR demonstrates significant improvements over state-of-the-art methods, proving the effectiveness of KDCDR in addressing data sparsity issues and enhancing cross-domain recommendation performance. Our code and data are available at https://***/pandas-bondage/KDCDR.

关键词： Cross-domain recommendation Knowledge distillation variational graph autoencoder

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Link Activation Using variational graph autoencoders

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IEEE COMMUNICATIONS LETTERS 2021年第7期25卷 2358-2361页

作者： Jamshidiha, Saeed Pourahmadi, Vahid Mohammadi, Abbas Bennis, Mehdi Amirkabir Univ Technol Dept Elect Engn Tehran 15914 Iran Univ Oulu Ctr Wireless Commun Oulu 90570 Finland

An unsupervised method is proposed for link activation in wireless networks by identifying clusters of interfering users. A k-nearest neighbors interference graph is first defined for the wireless network which is then mapped to a stochastic latent space. The users are then clustered in the latent space using a Gaussian mixture model, and one user from each interfering cluster is activated while the rest of the users in that cluster remain idle. The proposed framework is scalable, works across several network topologies such as device to device (D2D), and is close to the optimal solution in performance.

关键词： Interference Wireless networks Transmitters Device-to-device communication Stochastic processes Receivers Deep learning graph embedding variational graph autoencoder wireless networks Bayesian deep learning

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