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 ...
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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.
Recent applications of pattern recognition techniques to brain connectome-based classification focus on static functional connectivity (FC) neglecting the dynamics of FC over time, and use input connectivity matrices ...
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ISBN:
(数字)9781665496209
ISBN:
(纸本)9781665496209
Recent applications of pattern recognition techniques to brain connectome-based classification focus on static functional connectivity (FC) neglecting the dynamics of FC over time, and use input connectivity matrices on a regular Euclidean grid. We exploit the graph convolutional networks (GCNs) to learn irregular structural patterns in brain FC networks and propose extensions to capture dynamic changes in network topology. We develop a dynamic graph autoencoder (DyGAE)-based framework to leverage the time-varying topological structures of dynamic brain networks for identification of autism spectrum disorder (ASD). The framework combines a GCN-based DyGAE to encode individual-level dynamic networks into time-varying low-dimensional network embeddings, and classifiers based on weighted fully-connected neural network (FCNN) and long short-term memory (LSTM) to facilitate dynamic graph classification via the learned spatial-temporal information. Evaluation on a large ABIDE resting-state functional magnetic resonance imaging (rs-fMRI) dataset shows that our method outperformed state-of-the-art methods in detecting altered FC in ASD. Dynamic FC analyses with DyGAE learned embeddings also reveal apparent group difference between ASD and healthy controls in network profiles and switching dynamics of brain states.
Drug synergy is a crucial component in drug reuse since it solves the problem of sluggish drug development and the absence of corresponding drugs for several diseases. Predicting drug synergistic relationships can scr...
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Drug synergy is a crucial component in drug reuse since it solves the problem of sluggish drug development and the absence of corresponding drugs for several diseases. Predicting drug synergistic relationships can screen drug combinations in advance and reduce the waste of laboratory resources. In this research, we proposed a model that utilizes graph autoencoder and convolutional neural networks to predict drug synergy (GAECDS). Our methods include a graph convolutional neural network as an encoder to encode drug features and use a matrix factorization method as a decoder. Multilayer perceptron (MLP) was applied to process cell line features and combine them with drug features. Furthermore, the latent vectors generated during the encoding process are being used to predict drug synergistic scores using a convolutional neural network. By measuring prediction performance using AUC, AUPR, and F1 score, GAECDS superior to other state-of-the-art models. In addition, four pairs of the predicted top 10 drug combinations were found to work well enough for evaluation. The case study shows that the GAECDS approach is useful for identifying potential drug synergy. [graphICS] .
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...
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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.
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 effectivel...
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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.
In the last two decades, Light detection and ranging (LiDAR) has been widely employed in forestry applications. Individual tree segmentation is essential to forest management because it is a prerequisite to tree recon...
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In the last two decades, Light detection and ranging (LiDAR) has been widely employed in forestry applications. Individual tree segmentation is essential to forest management because it is a prerequisite to tree reconstruction and biomass estimation. This paper introduces a general framework to extract individual trees from the LiDAR point cloud based on a graph link prediction problem. First, an undirected graph is generated from the point cloud based on K-nearest neighbors (KNN). Then, this graph is used to train a convolutional autoencoder that extracts the node embeddings to reconstruct the graph. Finally, the individual trees are defined by the separate sets of connected nodes of the reconstructed graph. A key advantage of the proposed method is that no further knowledge about tree or forest structure is required. Seven sample plots from a plantation forest with poplar and dawn redwood species have been employed in the experiments. Though the precision of the experimental results is up to 95 % for poplar species and 92 % for dawn redwood trees, the method still requires more investigations on natural forest types with mixed tree species.
With the increasing automation and integration of equipment, it is urgent to carry out anomaly detection (AD) for the large-scale system to ensure security, in virtue of Industrial Internet of Things (IIoT). Recently ...
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With the increasing automation and integration of equipment, it is urgent to carry out anomaly detection (AD) for the large-scale system to ensure security, in virtue of Industrial Internet of Things (IIoT). Recently developed intelligent methods focus on component-level diagnosis or detection, resulting in difficulty in the health assessment of system with multisource data coupling. In addition, data-driven methods rarely emphasize the use of knowledge from the real physical system. In this article, we propose a full graph autoencoder to perform one-class group AD for the large-scale IIoT system. The proposed model takes as input data of normal status at training and only comprises several normalized graph convolutional layers, thus it is simple and fast. Different from Euclidean-based methods, the proposed model can handle various irregular structures together. For graph learning, multivariate time series are converted into graph data fused with prior knowledge. To achieve AD, we propose to reconstruct the full graph for the first time to obtain a reliable anomaly score. Besides, we extend a variational model to fully learn the graph representation. Moreover, a graph augmentation operation is employed to improve the accuracy and robustness. The proposed models are evaluated on two multisensor data sets from liquid rocket engine (LRE) systems, and the experimental results demonstrate the effectiveness and generalization of the IIoT system.
In recent years, with the rapid development of the Internet, the amount of data in the network increases exponentially, and people have higher requirements on the quality of network links, which poses new challenges t...
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ISBN:
(纸本)9789811996962;9789811996979
In recent years, with the rapid development of the Internet, the amount of data in the network increases exponentially, and people have higher requirements on the quality of network links, which poses new challenges to network management and analysis. Network link status classification can be used to predict network link status categories by using the current network topology information and feature information, facilitating network management and analysis. However, most of the existing models consider to predict the time sequence information of the link status without considering the topology information and node attributes of the network. Therefore, we design a network link status classification model based on graph autoencoder for computer network scenarios. The attention mechanism is introduced into the encoder, and then the node vector is spliced into edge vector, which is then input into the multi-layer perceptron for classification. Finally, the feasibility and validity of the link status classification model are verified on two datasets.
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-se...
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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.
Increasing evidence has suggested that microRNAs (miRNAs) are important biomarkers of various diseases. Numerous graph neural network (GNN) models have been proposed for predicting miRNA-disease associations. However,...
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Increasing evidence has suggested that microRNAs (miRNAs) are important biomarkers of various diseases. Numerous graph neural network (GNN) models have been proposed for predicting miRNA-disease associations. However, the existing GNN-based methods have over-smoothing issue-the learned feature embeddings of miRNA nodes and disease nodes are indistinguishable when stacking multiple GNN layers. This issue makes the performance of the methods sensitive to the number of layers, and significantly hurts the performance when more layers are employed. In this study, we resolve this issue by a novel self-feature-based graph autoencoder model, shortened as SFGAE. The key novelty of SFGAE is to construct miRNA-self embeddings and disease-self embeddings, and let them be independent of graph interactions between two types of nodes. The novel self-feature embeddings enrich the information of typical aggregated feature embeddings, which aggregate the information from direct neighbors and hence heavily rely on graph interactions. SFGAE adopts a graph encoder with attention mechanism to concatenate aggregated feature embeddings and self-feature embeddings, and adopts a bilinear decoder to predict links. Our experiments show that SFGAE achieves state-of-the-art performance. In particular, SFGAE improves the average AUC upon recent GAEMDA [] on the benchmark datasets HMDD v2.0 and HMDD v3.2, and consistently performs better when less (e.g. 10%) training samples are used. Furthermore, SFGAE effectively overcomes the over-smoothing issue and performs stably well on deeper models (e.g. eight layers). Finally, we carry out case studies on three human diseases, colon neoplasms, esophageal neoplasms and kidney neoplasms, and perform a survival analysis using kidney neoplasm as an example. The results suggest that SFGAE is a reliable tool for predicting potential miRNA-disease associations.
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