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检索条件"主题词=graph autoencoder"

共 111 条记录，以下是31-40 订阅

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Dual-Adaptive Fusion Multi-View Clustering Based on graph autoencoder

Dual-Adaptive Fusion Multi-View Clustering Based on Graph Au...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Niu, Changyong Wu, Mengqi Zhou, Lijuan Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou Peoples R China

ISBN: (纸本)9798350359329;9798350359312

The widespread application of multi-view graph data has facilitated the development of multi-view graph clustering. Effectively learning multi-view node representations is crucial for discovering inherent patterns in complex systems. However, most existing methods struggle to handle data with both multi-attribute and multi-relation simultaneously, while both attributes and relations are essential for graph clustering. Therefore, this paper proposes a dual-adaptive fusion multiview clustering method based on graph autoencoder. It utilizes multi-view encoders and decoders to encode and reconstruct inputs separately. Additionally, a dual-adaptive fusion module is introduced to integrate multi-view node representations. Through consistency clustering, the proposed method explores the probability distribution consistency among different views, thereby achieving consistent clustering results. Experimental results on three datasets demonstrate the effectiveness of the proposed method in clustering tasks.

关键词： multi-view learning node clustering graph autoencoder

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scGASI: A graph autoencoder-Based Single-Cell Integration Clustering Method 19th

scGASI: A Graph Autoencoder-Based Single-Cell Integration Cl...

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19th International Symposium on Bioinformatics Research and Applications

作者： Qiao, Tian-Jing Li, Feng Yuan, Shasha Dai, Ling-Yun Wang, Juan Qufu Normal Univ Sch Comp Sci Rizhao 276826 Peoples R China

ISBN: (纸本)9789819970735;9789819970742

Single-cell RNA sequencing (scRNA-seq) technology offers the opportunity to study biological issues at the cellular level. The identification of single-cell types by unsupervised clustering is a basic goal of scRNA-seq data analysis. Although there have been a number of recent proposals for single-cell clustering methods, only a few of these have considered both shallow and deep potential information. Therefore, we propose a graph autoencoder-based single-cell integration clustering method, scGASI. Based on multiple feature sets, scGASI unifies deep feature embedding and data affinity recovery in a uniform framework to learn a consensus affinity matrix between cells. scGASI first constructs multiple feature sets. Then, to extract the deep potential information embedded in the data, scGASI uses a graph autoencoder (GAEs) to learn the low-dimensional latent representation of the data. Next, to effectively fuse the deep potential information in the embedding space and the shallow information in the raw space, we design a multi-layer kernel self-expression integration strategy. This strategy uses a kernel self-expression model with multi-layer similarity fusion to learn a similarity matrix shared by the raw and embedding spaces of a given feature set, and a consensus learning mechanism to learn a consensus affinity matrix across all feature sets. Finally, the consensus affinity matrix is used for spectral clustering, visualization, and identification of gene markers. Large-scale validation on real datasets shows that scGASI has higher clustering accuracy than many popular clustering methods.

关键词： scRNA-seq Clustering graph autoencoder Multi-layer Kernel Self-expression Integration Multi-layer Similarity Fusion Consensus Learning

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graphCAR: Content-aware Multimedia Recommendation with graph autoencoder 18

GraphCAR: Content-aware Multimedia Recommendation with Graph...

引用

41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Xu, Qidi Shen, Fumin Liu, Li Shen, Heng Tao Univ Elect Sci & Technol China Ctr Future Media Chengdu Sichuan Peoples R China Univ East Anglia Sch Comp Sci Norwich Norfolk England

ISBN: (纸本)9781450356572

Precisely recommending relevant multimedia items from massive candidates to a large number of users is an indispensable yet difficult task on many platforms. A promising way is to project users and items into a latent space and recommend items via the inner product of latent factor vectors. However, previous studies paid little attention to the multimedia content itself and couldn't make the best use of preference data like implicit feedback. To fill this gap, we propose a Content-aware Multimedia Recommendation Model with graph autoencoder (graphCAR), combining informative multimedia content with user-item interaction. Specifically, user-item interaction, user attributes and multimedia contents (e.g., images, videos, audios, etc.) are taken as input of the autoencoder to generate the item preference scores for each user. Through extensive experiments on two real-world multimedia Web services: Amazon and Vine, we show that graphCAR significantly outperforms state-of-the-art techniques of both collaborative filtering and content-based methods.

关键词： Multimedia Recommendation graph autoencoder Content-based Filtering Implicit Feedback

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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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A graph autoencoder Approach to Crowdsourcing 13

A Graph Autoencoder Approach to Crowdsourcing

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13rd IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)

作者： Traganitis, Panagiotis A. Kanatsoulis, Charilaos I. Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Stanford Univ Dept Comp Sci Palo Alto CA 94304 USA

ISBN: (纸本)9798350344820;9798350344813

Crowdsourcing deals with combining and aggregating labels from crowds of annotators of unknown reliability. While most works on label aggregation operate under the assumption of independent and identically distributed data, the present work introduces an algorithm that operates under known data dependencies or correlations. To exploit these dependencies, a novel graph autoencoder-based algorithm is developed that fuses annotator labels for crowdsourced classification tasks. Numerical tests on real data showcase the potential of the proposed approach.

关键词： Crowdsourcing Weak supervision graph autoencoder graph neural networks Classification

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DNGAE: Deep Neighborhood graph autoencoder for Robust Blind Hyperspectral Unmixing 15th

DNGAE: Deep Neighborhood Graph Autoencoder for Robust Blind ...

引用

15th International Conference on Computational Collective Intelligence (ICCCI)

作者： Hanachi, Refka Sellami, Akrem Farah, Imed Riadh Mura, Mauro Dalla Univ Manouba RIADI Lab ENSI Manouba 2010 Tunisia Univ Lille Sci & Technol CRIStAL Lab Batiment Esprit F-59655 Villeneuve Dascq France IMT Atlant ITI Dept 655 Ave Technopole F-29280 Plouzane France Univ Grenoble Alpes Grenoble INP GIPSA Lab CNRS F-38000 Grenoble France

ISBN: (纸本)9783031414558;9783031414565

Recently, Deep Learning (DL)-based unmixing techniques have gained popularity owing to the robust learning of Deep Neural Networks (DNNs). In particular, the autoencoder (AE) model, as a baseline network for unmixing, performs well in Hyperspectral Unmixing (HU) by automatically learning a new representation and recovering original data. However, patch-wise AE based architecture, which incorporates both spectral and spatial information through convolutional filters may blur the abundance maps due to the fixed kernel shape of the used window size. To cope with the above issue, we propose in this paper a novel methodology based on graph DL called DNGAE. Unlike the pixel-wise or patch-wise Convolutional AE (CAE), our proposed method incorporates the complementary spatial information based on graph spectral similarity. A neighborhood graph based on band correlations is firstly constructed. Then, our method attempts to aggregate similar spectra from the neighboring pixels of a target pixel. Consequently, this leads to better quality of both extracted endmembers and abundances. Extensive experiments performed on two real HSI benchmarks confirm the effectiveness of our proposed method compared to other DL models.

关键词： Spectral unmixing Neighborhood graph Representation learning graph autoencoder

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Superpixel-Based Dual-Neighborhood Contrastive graph autoencoder for Deep Subspace Clustering of Hyperspectral Image 20th

Superpixel-Based Dual-Neighborhood Contrastive Graph Autoenc...

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20th International Conference on Intelligent Computing (ICIC)

作者： Li, Junhong Guan, Renxiang Han, Yuhang Hu, Yaowen Li, Zihao Wu, Yanyan Xu, Ziwei Li, Xianju China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Northeast Forestry Univ Coll Aulin Harbin 150040 Peoples R China

ISBN: (纸本)9789819755967;9789819755974

Deep subspace clustering of hyperspectral image (HSI) holds paramount importance for the fine classification of ground elements like land cover and geological units. While graph-based deep subspace learning effectively captures feature representations for clustering, it does not fully harness the global spatial information essential for large-scale imagery requiring multiple graph nodes for representation. This study proposes a Superpixel-based Dual-neighborhood Contrastivegraph autoencoder for Deep SubspaceClustering (SDCGSC), which consists of: (1) Superpixel-based dual-neighborhood graph autoencoder. Utilizing superpixels and their dual-neighborhood to construct two graphs facilitates the learning of local spatial information. (2) Contrastive graph learning. The model employs graph feature-level contrastive learning within each graph autoencoder branch and graph-level contrastive learning across branches to cultivate more robust features. (3) Self-expression reconstruction and clustering. Fused features from both branches, incorporating relative global information, are utilized for clustering. To validate the effectiveness of the proposed model, extensive experiments are conducted on multiple benchmark datasets. Results demonstrate that SDCGSC significantly outperformed existing state-of-the-art methods. In conclusion, superpixel and contrastive graph autoencoder-based deep subspace clustering is positive for HSI analysis.

关键词： Dual-neighborhood graph autoencoder superpixel hyperspectral image (HSI) subspace clustering contrastive learning

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A Self-supervised graph autoencoder with Barlow Twins 19th

A Self-supervised Graph Autoencoder with Barlow Twins

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19th Pacific Rim International Conference on Artificial Intelligence (PRICAI)

作者： Li, Jingci Lu, Guangquan Li, Jiecheng Guangxi Normal Univ Guangxi Key Lab Multi Source Informat Min & Secur Guilin 541004 Peoples R China

ISBN: (纸本)9783031208645;9783031208652

Self-supervised graph learning has attracted significant interest, especially graph contrastive learning. However, graph contrastive learning heavily relies on the choices of negative samples and the elaborate designs of architectures. Motivated by Barlow Twins, a method in computer vision, we propose a novel graph autoencoder named Core Barlow graph Auto-Encoder(CBGAE) which does not rely on any special techniques, like predictor networks or momentum encoders. Meanwhile, we set a core view to make maximize agreement between the learned feature information. In contrast to the most existing graph contrastive learning models, it is negative-sample-free.

关键词： graph autoencoder graph representation learning Self-supervised learning Barlow Twins

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MGAE: Marginalized graph autoencoder for graph Clustering 17

MGAE: Marginalized Graph Autoencoder for Graph Clustering

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ACM Conference on Information and Knowledge Management (CIKM)

作者： Wang, Chun Pan, Shirui Long, Guodong Zhu, Xingquan Jiang, Jing Univ Technol Sydney Ctr Artificial Intelligence Sydney NSW 2007 Australia Florida Atlantic Univ Dept CEECS Boca Raton FL 33431 USA

ISBN: (纸本)9781450349185

graph clustering aims to discover community structures in networks, the task being fundamentally challenging mainly because the topology structure and the content of the graphs are difficult to represent for clustering analysis. Recently, graph clustering has moved from traditional shallow methods to deep learning approaches, thanks to the unique feature representation learning capability of deep learning. However, existing deep approaches for graph clustering can only exploit the structure information, while ignoring the content information associated with the nodes in a graph. In this paper, we propose a novel marginalized graph autoencoder (MGAE) algorithm for graph clustering. The key innovation of MGAE is that it advances the autoencoder to the graph domain, so graph representation learning can be carried out not only in a purely unsupervised setting by leveraging structure and content information, it can also be stacked in a deep fashion to learn effective representation. From a technical viewpoint, we propose a marginalized graph convolutional network to corrupt network node content, allowing node content to interact with network features, and marginalizes the corrupted features in a graph autoencoder context to learn graph feature representations. The learned features are fed into the spectral clustering algorithm for graph clustering. Experimental results on benchmark datasets demonstrate the superior performance of MGAE, compared to numerous baselines.

关键词： graph clustering autoencoder graph autoencoder graph convolutional network network representation

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Multi-Scale Variational graph autoencoder for Link Prediction 22

Multi-Scale Variational Graph AutoEncoder for Link Predictio...

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15th ACM International Conference on Web Search and Data Mining (WSDM)

作者： Guo, Zhihao Wang, Feng Yao, Kaixuan Liang, Jiye Wang, Zhiqiang Shanxi Univ Sch Comp & Informat Technol Taiyuan Shanxi Peoples R China

ISBN: (纸本)9781450391320

Link prediction has become a significant research problem in deep learning, and the graph-based autoencoder model is one of the most important methods to solve it. The existing graph-based autoencoder models only learn a single set of distributions, which cannot accurately represent the mixed distribution in real graph data. Meanwhile, existing learning models have been greatly restricted when the graph data has insufficient attribute information and inaccurate topology information. In this paper, we propose a novel graph embedding framework, termed multi-scale variational graph autoencoder (MSVGAE), which learns multiple sets of low-dimensional vectors of different dimensions through the graph encoder to represent the mixed probability distribution of the original graph data, and performs multiple sampling in each dimension. Furthermore, a self-supervised learning strategy (i.e., graph feature reconstruction auxiliary learning) is introduced to fully use the graph attribute information to help the graph structure learning. Experiment studies on real-world graphs demonstrate that the proposed model achieves state-of-the-art performance compared with other baseline methods in link prediction tasks. Besides, the robustness analysis shows that the proposed MSVGAE method has obvious advantages in the processes of graph data with insufficient attribute information and inaccurate topology information.

关键词： graph autoencoder Self-Supervised Learning graph Neural Networks Link Prediction

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