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variational graph autoencoder-driven balancing strategy for multimodal multi-objective optimization

INFORMATION SCIENCES 2025年 712卷

作者： Yang, Lei Zhang, Erlei Dang, Qianlong Northwest A&F Univ Coll Informat Engn Yangling 712100 Peoples R China Northwest A&F Univ Coll Sci Yangling 712100 Peoples R China

Multimodal multi-objective optimization aims to balance the diversity and the convergence to obtain multiple complete and uniform Pareto optimal solution sets. In recent years, using machine learning models to improve the performance of evolutionary algorithms has become a hot topic. However, few studies utilize machine learning models to solve the imbalance problem between the diversity and the convergence in multimodal multi-objective optimization. Therefore, this paper proposes a multimodal multi-objective evolutionary algorithm driven by variational graph autoencoder (VGAE), which can reproduce diversified offspring with good convergence by reconstructing the parent population. In reproduction, the parent population is constructed into graph data, and the VGAE is adopted to map the graph data to the latent space, obtaining the distribution information represented by the low-dimensional vector. By sampling the distribution, the VGAE can generate the offspring with the similar distribution to the parent, which can fill the less dense regions in the decision space and improve the exploitation ability. In archive updating, the convergence state based on the inverted generation distance between the non-dominated solutions and the worst dominated solutions is defined, and the state information of the convergence archive is transferred to the diversity archive to determine the dynamic niche. This niche comprehensively considers the distribution state and convergence degree of solutions in the diversity and convergence archives, which is employed to calculate the local convergence quality, retaining more promising solutions. The results of 48 benchmark problems and a practical application show that the proposed algorithm outperforms eight competitive algorithms.

关键词： Multimodal multi-objective optimization Imbalance problem variational graph autoencoder Inverted generation distance Dynamic niche

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Dual stream fusion link prediction for sparse graph based on variational graph autoencoder and pairwise learning

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INFORMATION PROCESSING & MANAGEMENT 2025年第3期62卷

作者： Li, Xun Cai, Hongyun Feng, Chuan Zhao, Ao Hebei Univ Sch Cyber Secur & Comp Baoding 071000 Hebei Peoples R China Hebei Univ Key Lab High Trusted Informat Syst Hebei Prov Baoding 071000 Hebei Peoples R China

Recently, link prediction methods based graph neural networks have garnered significant attention and achieved great success on large datasets. However, existing methods usually rely on explicit graph structures, which is hard to obtain in sparse graphs. In addition, the incomplete graph data used for model training may lead to distribution shift between training and testing sets. To address these issues, this paper proposes a novel link prediction method for sparse graphs based on variational graph autoencoder and pairwise learning. By incorporating noise perturbation variational autoencoders, the proposed method can enhance robustness during sparse graph training. Instead of relying on explicit graph features, we reconstruct the original adjacency matrix by disturbing node feature mean encoding or variance encoding. To mitigate the impact of insufficient topological information, we introduce pairwise learning scheme, which obtains pairwise edges through negative sampling and iteratively optimize the positive and negative complementary probability adjacency matrix. Furthermore, we integrate the probability adjacency matrix and node similarity prediction based on message passing networks into a dual-stream framework to predict unknown links. Experimental results on multiple sparse networks demonstrate the superior link prediction performance of our proposed method over baseline approaches. Our method improves AUC from 0.3% to 1.5% and Precision from 1.4% to 5.2% across seven datasets.

关键词： Link prediction Sparse graph graph neural networks variational graph autoencoder Pairwise learning

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Reconstruction and Generation of Porous Metamaterial Units Via variational graph autoencoder and Large Language Model

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2025年第2期25卷 021003页

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

In this paper, we propose and compare two novel deep generative model-based approaches for the design representation, reconstruction, and generation of porous metamaterials characterized by complex and fully connected solid and pore networks. A highly diverse porous metamaterial database is curated, with each sample represented by solid and pore phase graphs and a voxel image. All metamaterial samples adhere to the requirement of complete connectivity in both pore and solid phases. The first approach employs a dual decoder variational graph autoencoder to generate both solid phase and pore phase graphs. The second approach employs a variational graph autoencoder for reconstructing/generating the nodes in the solid phase and pore phase graphs and a transformer-based large language model (LLM) for reconstructing/generating the connections, i.e., the edges among the nodes. A comparative study was conducted, and we found that both approaches achieved high accuracy in reconstructing node features, while the LLM exhibited superior performance in reconstructing edge features. Reconstruction accuracy is also validated by voxel-to-voxel comparison between the reconstructions and the original images in the test set. Additionally, discussions on the advantages and limitations of using LLMs in metamaterial design generation, along with the rationale behind their utilization, are provided.

关键词： porous metamaterial graph representation graph neural network large language model variational graph autoencoder artificial intelligence

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Adversarial Attention-Based variational graph autoencoder

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IEEE ACCESS 2020年 8卷 152637-152645页

作者： Weng, Ziqiang Zhang, Weiyu Dou, Wei Shandong Acad Sci Qilu Univ Technol Sch Comp Sci & Technol Jinan 250353 Peoples R China

autoencoders have been successfully used for graph embedding, and many variants have been proven to effectively express graph data and conduct graph analysis in low-dimensional space. However, previous methods ignore the structure and properties of the reconstructed graph, or they do not consider the potential data distribution in the graph, which typically leads to unsatisfactory graph embedding performance. In this paper, we propose the adversarial attention variational graph autoencoder (AAVGA), which is a novel framework that incorporates attention networks into the encoder part and uses an adversarial mechanism in embedded training. The encoder involves node neighbors in the representation of nodes by stacking attention layers, which can further improve the graph embedding performance of the encoder. At the same time, due to the adversarial mechanism, the distribution of the potential features that are generated by the encoder are closer to the actual distribution of the original graph data;thus, the decoder generates a graph that is closer to the original graph. Experimental results prove that AAVGA performs competitively with state-of-the-art popular graph encoders on three citation datasets.

关键词： Attention layers adversarial mechanism variational graph autoencoder

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Predicting Protein-Protein Interactions Using Sequence and Network Information via variational graph autoencoder

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第5期20卷 3182-3194页

作者： Luo, Xin Wang, Liwei Hu, Pengwei Hu, Lun Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Guangdong Peoples R China Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830000 Peoples R China

Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negative rates observed in PPI data. To overcome this problem, a novel PPI prediction algorithm, namely PASNVGA, is proposed in this work by combining the sequence and network information of proteins via variational graph autoencoder. To do so, PASNVGA first applies different strategies to extract the features of proteins from their sequence and network information, and obtains a more compact form of these features using principal component analysis. In addition, PASNVGA designs a scoring function to measure the higher-order connectivity between proteins and so as to obtain a higher-order adjacency matrix. With all these features and adjacency matrices, PASNVGA trains a variational graph autoencoder model to further learn the integrated embeddings of proteins. The prediction task is then completed by using a simple feedforward neural network. Extensive experiments have been conducted on five PPI datasets collected from different species. Compared with several state-of-the-art algorithms, PASNVGA has been demonstrated as a promising PPI prediction algorithm.

关键词： Protein-protein Interaction PPI prediction PPI network Protein sequences variational graph autoencoder

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variational graph autoencoder with Mutual Information Maximization for graph Representations Learning

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2022年第9期36卷 2252012-2252012页

作者： Li, Dongjie Li, Dong Lian, Guang South China Univ Technol Sch Software Engn 382 Wai Huan East Rd Guangzhou Guangdong Peoples R China

graph neural network (GNN) is a powerful representation learning framework for graph-structured data. Some GNN-based graph embedding methods, including variational graph autoencoder (VGAE), have been presented recently. However, existing VGAE-based methods typically focus on reconstructing the adjacent matrix, i.e. topological structure, instead of the node features matrix, this strategy makes graphical features difficult to be fully learned, which weakens and restricts the capacity of a generative network to learn higher-quality representations. To address the issue, we use a contrastive estimator on the representation mechanism, i.e. on the encoding process under the framework of VGAE. In particular, we maximize the mutual information (MI) between encoded latent representation and node attributes which acts as a regularizer forcing the encoder to select the most informative with respect to the node attributes. Additionally, we also solve another key question how to effectively estimate the mutual information by drawing samples from the joint and marginal, and explain why the maximization of MI can contribute to the encoder obtaining more node feature information. Ultimately, extensive experiments on three citation networks and four web-age networks show that our method outperforms contemporary popular algorithms (such as DGI) on node classifications and clustering tasks, and the best result is an 8.28% increase on node clustering task.

关键词： variational graph autoencoder graph representations learning maximize mutual information graphical feature learning

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Identification of microbe-disease signed associations via multi-scale variational graph autoencoder based on signed message propagation

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BMC BIOLOGY 2024年第1期22卷 1页

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

BackgroundPlenty of clinical and biomedical research has unequivocally highlighted the tremendous significance of the human microbiome in relation to human health. Identifying microbes associated with diseases is crucial for early disease diagnosis and advancing precision *** that the information about changes in microbial quantities under fine-grained disease states helps to enhance a comprehensive understanding of the overall data distribution, this study introduces MSignVGAE, a framework for predicting microbe-disease sign associations using signed message propagation. MSignVGAE employs a graph variational autoencoder to model noisy signed association data and extends the multi-scale concept to enhance representation capabilities. A novel strategy for propagating signed message in signed networks addresses heterogeneity and consistency among nodes connected by signed edges. Additionally, we utilize the idea of denoising autoencoder to handle the noise in similarity feature information, which helps overcome biases in the fused similarity data. MSignVGAE represents microbe-disease associations as a heterogeneous graph using similarity information as node features. The multi-class classifier XGBoost is utilized to predict sign associations between diseases and *** achieves AUROC and AUPR values of 0.9742 and 0.9601, respectively. Case studies on three diseases demonstrate that MSignVGAE can effectively capture a comprehensive distribution of associations by leveraging signed information.

关键词： variational graph autoencoder Microbe-disease association Signed message propagation XGBoost

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PHYSICS-GUIDED variational graph autoencoder FOR AIR QUALITY INFERENCE 49

PHYSICS-GUIDED VARIATIONAL GRAPH AUTOENCODER FOR AIR QUALITY...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Rodrigo-Bonet, Esther Deligiannis, Nikos Vrije Univ Brussel Dept Elect & Informat Pleinlaan 2 B-1050 Brussels Belgium imec Kapeldreef 75 B-3001 Leuven Belgium

ISBN: (纸本)9798350344868;9798350344851

Urban air quality modelling aims at inferring unknown pollution concentrations at specific urban locations. Physical methods derive the partial differential equations (PDEs) that mathematically define the laws of motion, albeit using computationally intense algorithms. In contrast, deep (generative) models, such as variational autoencoders, provide high performance by addressing the task as a data generation problem. Yet, physics knowledge and the spatio-temporal data correlations are not exploited by these deep learning models. In this work, we propose a physics-guided variational graph autoencoder whose graph convolutional operator is derived from the PDE defining the convection-diffusion physical process. We compare against statistical and deep learning approaches on two air quality datasets and report superior performance.

关键词： Physics-informed deep learning geometric deep learning variational graph autoencoder graph signal processing

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Epitomic variational graph autoencoder 25

Epitomic Variational Graph Autoencoder

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25th International Conference on Pattern Recognition (ICPR)

作者： Khan, Rayyan Ahmad Anwaar, Muhammad Umer Kleinsteuber, Martin Tech Univ Munich Munich Germany Mercateo AG Munich Germany

ISBN: (纸本)9781728188089

variational autoencoder (VAE) is a widely used generative model for learning latent representations. Burda et al. [3] in their seminal paper showed that learning capacity of VAE is limited by over-pruning. It is a phenomenon where a significant number of latent variables fail to capture any information about the input data and the corresponding hidden units become inactive. This adversely affects learning diverse and interpretable latent representations. As variational graph autoencoder (VGAE) extends VAE for graph-structured data, it inherits the over-pruning problem. In this paper, we adopt a model based approach and propose epitomic VGAE (EVGAE), a generative variational framework for graph datasets which successfully mitigates the over-pruning problem and also boosts the generative ability of VGAE. We consider EVGAE to consist of multiple sparse VGAE models, called epitomes, that are groups of latent variables sharing the latent space. This approach aids in increasing active units as epitomes compete to learn better representation of the graph data. We verify our claims via experiments on three benchmark datasets. Our experiments show that EVGAE has a better generative ability than VGAE. Moreover, EVGAE outperforms VGAE on link prediction task in citation networks.

关键词： graph autoencoder variational graph autoencoder graph neural networks Over-pruning VAE EVGAE

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scGMM-VGAE: a Gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell RNA-seq data

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第3期4卷 035013页

作者： Lin, Eric Liu, Boyuan Lac, Leann Fung, Daryl L. X. Leung, Carson K. Hu, Pingzhao Univ Toronto Dalla Lana Sch Publ Hlth Toronto ON Canada Univ Manitoba Dept Comp Sci Winnipeg MB Canada Univ Manitoba Dept Stat Winnipeg MB Canada Univ Manitoba Dept Biochem & Med Genet Winnipeg MB Canada Western Univ Dept Biochem London ON N6A 5C1 Canada

Cell type identification using single-cell RNA sequencing data is critical for understanding disease mechanisms and drug discovery. Cell clustering analysis has been widely studied in health research for rare tumor cell detection. In this study, we propose a Gaussian mixture model-based variational graph autoencoder on scRNA-seq data (scGMM-VGAE) that integrates a statistical clustering model to a deep learning algorithm to significantly improve the cell clustering performance. This model feeds a cell-cell graph adjacency matrix and a gene feature matrix into a graph variational autoencoder (VGAE) to generate latent data. These data are then used for cell clustering by the Gaussian mixture model (GMM) module. To optimize the algorithm, a designed loss function is derived by combining parameter estimates from the GMM and VGAE. We test the proposed method on four publicly available and three simulated datasets which contain many biological and technical zeros. The scGMM-VGAE outperforms four selected baseline methods on three evaluation metrics in cell clustering. By successfully incorporating GMM into deep learning VGAE on scRNA-seq data, the proposed method shows higher accuracy in cell clustering on scRNA-seq data. This improvement has a significant impact on detecting rare cell types in health research. All source codes used in this study can be found at .

关键词： Gaussian mixture model variational graph autoencoder single cell RNA-seq scGMM-VGAE cell type model integration

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