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graph attention autoencoder inspired CNN based brain tumor classification using MRI

NEUROCOMPUTING 2022年 503卷 236-247页

作者： Mishra, Lalita Verma, Shekhar Indian Inst Informat Technol Allahabad 211015 Uttar Pradesh India

Early and accurate detection is a solitary precaution to overcome the brain tumor. Otherwise, it will result in a deadly disease. Brain tumor (BT) detection using magnetic resonance is an essential and challenging job in the medical domain. To combat the aggressive spreading of BT, clinical imaging (MRI and X-Ray) can be an appropriate method for diagnosis. In this paper, we applied attention based image classification, which is a new paradigm for obtaining improved accuracy than the state-of-the-art models. We propose a GATE-CNN (graph attention autoencoder-Convolution Neural Network) model in this work for the classification of BT. We calculate the attention values of the neighboring pixels on each and every pixel present in the graph then process the graph using GATE framework and the processed graph with attention values is then passed to CNN framework for generation of final output. Further, we apply Adamax optimizer to optimize the training hyperparameters of CNN. We perform the BT classification using the proposed method on three different datasets among benign and malignant BT for first dataset, glioma and pituitary for second dataset, and normal and abnormal BT images for third dataset;all the three datasets consists of MRI images. We then compare the proposed model with a variety of CNN (Deep-CNN, Multi-Input CNN, and basic CNN) models dealing with several types of medical imaging (thermal image, MRI, X-Ray, etc.). Our model results in the accuracy of 98.27% for first dataset, 99.83% for second dataset, and 98.78% for third dataset demonstrating preferable network performance than the already established state-of-the-art CNN models. (C) 2022 Elsevier B.V. All rights reserved.

关键词： graph attention autoencoder Convolution neural network Brain tumor detection Machine learning Medical imaging Image processing Magnetic resonance image

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Overlapping Community Detection in Vehicular Social Networks Based on graph attention autoencoder

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SENSORS 2025年第8期25卷 2601-2601页

作者： Gu, Xiang Huang, Qiwei Yang, Jie Nantong Univ Sch Artificial Intelligence & Comp Sci Nantong 226019 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

Community detection is particularly important in vehicular social networks because it helps identify closely connected groups of vehicles within the network. Community structures with overlapping relationships are identified through network topology and vehicle attribute information, thereby optimizing communication efficiency, supporting resource allocation, and enhancing privacy protection. However, most existing community detection methods focus on non-overlapping communities, usually only considering the topological structure of the network, and often ignoring the attribute information of nodes. To address these problems, this paper proposes a semi-supervised overlapping community detection method based on graph attention autoencoder (CDGAAE). The method consists of three key components: graph attention autoencoder module, modularity optimization enhancement module, and semi-supervised clustering module. First, the graph attention autoencoder module fuses topological information and node attribute information and encodes nodes using a graph attention mechanism. Second, the modularity optimization enhancement module effectively captures the structure of overlapping communities. Finally, the semi-supervised clustering module combines prior information to improve the accuracy of community detection. CDGAAE is comprehensively evaluated on multiple real and synthetic datasets, and experimental results show that CDGAAE outperforms other competing methods.

关键词： graph attention autoencoder overlapping community detection semi-supervised clustering vehicular social networks

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Unsupervised graph attention autoencoder clustering-oriented for community detection in attributed networks

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2025年 1-14页

作者： Bekkair, Abdelfateh Bellaouar, Slimane Oulad-Naoui, Slimane Univ Ghardaia Lab Math & Sci Appl LMSA Ghardaia Algeria Univ Ghardaia Fac Sci & Technol Ghardaia Algeria

Networks are often used to represent complex systems and a wide range of natural phenomena. Uncovering the community structure of these networks is an essential task for comprehending them. While numerous algorithms for community detection have been suggested, graph neural networks (GNNs) have recently emerged as a promising method for improving this task. This paper presents a straightforward and effective model called Unsupervised graph attention autoencoder Clustering-Oriented (G2ACO) for community detection in attributed networks. The model adeptly captures representations from both the network's topology and attribute data, effectively addressing two simultaneous objectives: reconstruction and community detection. The community detection objective utilizes the k-means algorithm as a loss function in the autoencoder to guide representation in the clustering task. Additionally, the model consists of an encoder with a multi-head graph attention and inner product decoder. Our approach surpasses competitive algorithms in terms of NMI and ARI measures based on experiments conducted on three attributed citation network datasets and a prominent social network dataset. Empirically, our method achieves a faster runtime than the most competitive model as the network size increases, highlighting its practical potential for real-world applications. The significance of our results extends beyond biological network interpretation and social network analysis, where understanding the fundamental community structure is crucial.

关键词： Community detection graph attention autoencoder Representation learning Attributed networks

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scGAAC: A graph attention autoencoder for clustering single-cell RNA-sequencing data

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METHODS 2024年 229卷 115-124页

作者： Zhang, Lin Xiang, Haiping Wang, Feng Chen, Zepeng Shen, Mo Ma, Jiani Liu, Hui Zheng, Hongdang China Univ Min & Technol Xuzhou 221116 Peoples R China Univ Melbourne Melbourne Vet Sch Dept Vet Biosci Parkville Vic 3010 Australia

Single-cell RNA-sequencing (scRNA-seq) enables the investigation of intricate mechanisms governing cell heterogeneity and diversity. Clustering analysis remains a pivotal tool in scRNA-seq for discerning cell types. However, persistent challenges arise from noise, high dimensionality, and dropout in single-cell data. Despite the proliferation of scRNA-seq clustering methods, these often focus on extracting representations from individual cell expression data, neglecting potential intercellular relationships. To overcome this limitation, we introduce scGAAC, a novel clustering method based on an attention-based graph convolutional autoencoder. By leveraging structural information between cells through a graph attention autoencoder, scGAAC uncovers latent relationships while extracting representation information from single-cell gene expression patterns. An attention fusion module amalgamates the learned features of the graph attention autoencoder and the autoencoder through attention weights. Ultimately, a self-supervised learning policy guides model optimization. scGAAC, a hypothesis-free framework, performs better on four real scRNA-seq datasets than most state-of-the-art methods. The scGAAC implementation is publicly available on Github at: https://***/labiip/scGAAC.

关键词： scRNA-seq clustering graph attention autoencoder Self-supervised learning

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A multi-agent flocking collaborative control method for stochastic dynamic environment via graph attention autoencoder based reinforcement learning

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NEUROCOMPUTING 2023年第1期549卷

作者： Xiao, Jian Yuan, Guohui Wang, Zhuoran Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Quzhou Quzhou 324000 Zhejiang Peoples R China Quzhou Univ Coll Elect & Informat Engn Quzhou 324000 Zhejiang Peoples R China

The environmental adaptability of the multi-agent flocking collaborative control system is vital to practical applications. Focusing on the adaptive problem of multi-agent flocking collaborative control system in stochastic dynamic environment, this paper proposes a distributed multi-agent flocking collaborative control algorithm based on a graph attention autoencoder (GAE) based multi-agent reinforcement learning (MARL). In our algorithm, a distance-based graph attention (GAT) mechanism is introduced into the networks of MARL to improve the non-stationarity problem of state transition in MARL caused by stochastic dynamic environment and enhance agents' comprehension to the observation state. Based on the distance-based GAT, a GAE is designed to adapt to dynamic scale scenes. In addition, a global reward based strategy evaluation method is used to minimize the system loss of the flocking collaborative control system. The experimental results demonstrate that the proposed flocking algorithm has better environmental adaptability and better global control strategy than other RL-based flocking algorithms. The conclusion that the control strategies learned by our algorithm can be well transferred to the scenes with different agents and obstacles is also verified. This paper provides a novel and effective solution scheme to the multi-agent flocking collaborative control problem in a stochastic dynamic environment, which is conducive to promoting the application of flocking algorithm.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Multi-agent flocking collaborative control & nbsp system Stochastic dynamic environment Multi-agent reinforcement learning graph attention autoencoder Global reward based strategy evaluation

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scGADR: Dimensionality Reduction of Single-Cell RNA-seq Data with ZINB-Based graph attention autoencoder 20th

scGADR: Dimensionality Reduction of Single-Cell RNA-seq Data...

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20th International Conference on Intelligent Computing (ICIC)

作者： Tang, Yongxuan Luo, Jiawei Xu, Zhongyuan Xiong, Zehao Wang, Bo Nguyen Hoang Tu Hunan Univ Coll Comp Sci & Elect Engn Changsha 410083 Hunan Peoples R China Hanoi Univ Ind Fac Informat & Technol Hanoi 100803 Vietnam

ISBN: (纸本)9789819756919;9789819756926

Due to the high variability, high sparsity, and high dimensionality of single-cell RNA sequencing (scRNA-seq) data, dimensionality reduction is critical for visualizing and interpreting the high-dimensional scRNA-seq data. Herein, we propose a new single-cell graph attention dimensionality reduction method (scGADR) that utilizes graph attention autoencoders to reduce the dimensionality of the input data. scGADR employs the graph attention network to assign different weights to the cell's neighbor nodes according to their importance and incorporates the zero-inflated negative binomial (ZINB) distribution into the model's decoder to learn the low-dimensional latent representation of the original data. Additionally, the Kullback-Leibler (KL) algorithm facilitates self-optimizing iterative training. The effectiveness of scGADR is validated through downstream experiments on low-dimensional representation, demonstrating its superiority over six state-of-the-art dimensionality reduction methods on 13 representative datasets generated by different sequencing platforms.

关键词： Single-cell RNA-seq graph attention autoencoder Dimensionality reduction Zero-inflated negative binomial

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graph attention autoencoder for Collaborative Pair-wise Ranking 24

Graph Attention Autoencoder for Collaborative Pair-wise Rank...

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24th IEEE International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD)

作者： Mu, Nan Zha, Daren He, Yuanye Zhao, Lin Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781728165974

Recently, top-k recommendation system is getting more and more attention from researchers and unlike the rating prediction task, the purpose of top-k recommendation is to present the user with a list of items they are most interested in. Many rating prediction models do not produce good ranking results, so the top-k recommendation faces two challenges: the first is how to accurately obtain user and item latent vector from the user-item interaction rating matrix, and the second challenge is how to combine the ranking strategy with the recommendation model deeply. The booming deep learning technology, such as autoencoder and graph neural networks, brings us new solutions. So in this paper, we propose a novel model: graph attention autoencoder for Collaborative Pair-wise Ranking. The autoencoder consists of graph attention encoder and collaborative neural decoder, which is used to generate user and item latent vector accurately. And then we use the pairwise ranking learning process to ensure the rating value accuracy and rating value pairwise ranking consistency. Finally, experimental results on three real-world datasets demonstrate the superiority of our model.

关键词： graph attention autoencoder Pair-wise Ranking Multi-head attention Top-K Recommendation

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scVAG: Unified single-cell clustering via variational-autoencoder integration with graph attention autoencoder

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Heliyon 2024年第23期10卷 e40732页

作者： Laghaee, Seyedpouria Eskandarian, Morteza Fereidoon, Mohammadamin Koohi, Somayyeh Department of Computer Engineering Sharif University of Technology Tehran Tehran *** Iran Department of Computer Science University of Tehran Tehran Tehran *** Iran

Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptional profiling of cell heterogeneity. However, analyzing this noisy, high-dimensional matrix remains challenging. We present scVAG, an integrated deep learning framework combining Variational-autoencoder (VAE) and graph attention autoencoder (GATE) for enhanced single-cell clustering. Building upon scGAC, our approach replaces its restrictive linear principal component analysis (PCA) with nonlinear dimensionality reduction better suited for scRNA-seq data. Specifically, we integrate VAE and GATE to enable more flexible latent space encoding. Extensive experiments on 20 datasets demonstrate scVAG's superior performance over previous state-of-the-art methods including scGAC, SCEA, SC3, Seurat, scGNN, scASGC, DESC, NIC, scLDS2, DRJCC, sLMIC, and jSRC. On average, scVAG improves clustering accuracy by 5 percent in ARI and 4 percent in NMI parameters. Visualizations highlight scVAG's capacity to recover interpretable biological structures. Our VAE-GATE pipeline extracts intricate expression patterns into compact representations that precisely delineate cell subpopulations consistent with ground truth labels. Overall, scVAG establishes a robust architecture for elucidating cell taxonomies from noisy transcriptomic inputs. © 2024 The Authors

关键词： Clustering Dimensionality reduction graph attention autoencoder Single-cell RNA sequencing Variational-autoencoder

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Cluster Analysis of Scrna-Seq Data Combining Bioinformatics with graph attention autoencoders and Ensemble Clustering 20th

Cluster Analysis of Scrna-Seq Data Combining Bioinformatics ...

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20th International Conference on Intelligent Computing (ICIC)

作者： Yuan, Lin Xu, Zhijie Li, Zhujun Zhang, Shoukang Hu, Chunyu Yu, Wendong Wei, Hongwei Wang, Xingang Geng, Yushui Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci Ctr Minist EducKey Lab Comp Power Network & Informat Jinan Peoples R China Qilu Univ Technol Shandong Acad Sci Fac Comp Sci & Technol Shandong Engn Res Ctr Big Data Appl Technol Jinan Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Comp Networks Jinan Peoples R China Jinan Springs Patent & Trademark Off Jinan Peoples R China Shandong Tianyi Informat Technol Co Ltd Jinan Peoples R China

ISBN: (纸本)9789819756919;9789819756926

As single-cell RNA sequencing (scRNA-seq) technology has rapidly become a powerful technique for revealing gene expression information at the cellular level. In scRNA-seq data analysis, cell clustering is a key step in downstream analysis as it can identify cell types and discover new cell subtypes. However, the high dimensionality, sparsity, and high noise characteristics of scRNA-seq datasets present significant challenges for clustering *** this study, a model based on bipartite graph integration clustering and graph attention autoencoder is proposed. Firstly, the scRNA-seq dataset is preprocessed using network enhancement (NE) and principal component analysis (PCA) for denoising and feature selection. Next, a graph attention autoencoder is employed for dimension reduction to obtain low-dimensional embeddings. Finally, bipartite graph integration clustering is utilized to derive the final clustering results based on the relationship between cells and low-dimensional embeddings. Based on various clustering metrics, a comparison was made between different scRNA-seq datasets, and the experimental results showed that scBAGA outperformed other advanced methods. This indicates that our model can serve as a reliable classification tool.

关键词： scRNA-seq Clustering analysis graph attention autoencoder integrated clustering

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scCAT: Single-cell Combined graph attentional clustering for scRNA-seq analysis

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 105卷

作者： Gao, Yufei Zhang, Wenbo Zhang, Yameng Shi, Yujie Shi, Lei Wang, Hailing Cheng, Guozhen Henan Prov Peoples Hosp Dept Pathol Zhengzhou 450003 Henan Peoples R China Zhengzhou Univ Peoples Hosp Zhengzhou 450003 Henan Peoples R China Zhengzhou Univ Sch Cyber Sci & Engn Zhengzhou 450001 Henan Peoples R China SongShan Lab Zhengzhou 450001 Henan Peoples R China Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201600 Peoples R China Informat Engn Univ Inst Informat Technol Zhengzhou 450002 Henan Peoples R China

The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized gene expression research at the single-cell level, enabling the study of cellular heterogeneity and identification of rare cell populations. Deep clustering is crucial for analyzing scRNA-seq datasets by assigning cells into subpopulations. However, inherent sparsity and variability in gene expression pose challenges to clustering accuracy. To address these issues, a novel unsupervised deep clustering approach named single-cell Combined graph attentional clustering (scCAT) is introduced. The method designs a dual-branch joint dimensionality reduction (JDR) module to learn gene expression. This strategy preserves key variance while capturing complex nonlinear relationships, effectively addressing the high-dimensionality challenges of single-cell data. Additionally, a Zero-inflated negative binomial (ZINB) distribution is integrated within the JDR to tackle significant noise, sparsity, and zero-inflation challenges. A graph attention autoencoder (GATE) then processes the graph-structured data, enhancing the integration of integrating cellular topological relationships and gene expressions. Finally, a kmeans-based self-optimization technique refines clustering while synchronizing with representation learning. Experimental evaluations on eight scRNA-seq datasets demonstrate that scCAT significantly improves clustering performance, mitigates the impact of inherent data defects. The embeddings learned from both linear and non-linear perspectives eliminate inter-component interactions, uncovering complex underlying structures and enhancing the recognition of cellular topological relationships when combined with graph neural networks.

关键词： scRNA-seq Deep clustering graph attention autoencoder Variational autoencoder Zero-inflated negative binomial model

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