Journal articles: 'Deep graph clustering'

1

Zhang, Xiaoran, Xuanting Xie, and Zhao Kang. "Graph Learning for Attributed Graph Clustering." Mathematics 10, no. 24 (December 19, 2022): 4834. http://dx.doi.org/10.3390/math10244834.

Full text

Abstract:

Due to the explosive growth of graph data, attributed graph clustering has received increasing attention recently. Although deep neural networks based graph clustering methods have achieved impressive performance, the huge amount of training parameters make them time-consuming and memory- intensive. Moreover, real-world graphs are often noisy or incomplete and are not optimal for the clustering task. To solve these problems, we design a graph learning framework for the attributed graph clustering task in this study. We firstly develop a shallow model for learning a fine-grained graph from smoothed data, which sufficiently exploits both node attributes and topology information. A regularizer is also designed to flexibly explore the high-order information hidden in the data. To further reduce the computation complexity, we then propose a linear method with respect to node number n, where a smaller graph is learned based on importance sampling strategy to select m(m≪n) anchors. Extensive experiments on six benchmark datasets demonstrate that our proposed methods are not only effective but also more efficient than state-of-the-art techniques. In particular, our method surpasses many recent deep learning approaches.

APA, Harvard, Vancouver, ISO, and other styles

2

Tu, Wenxuan, Sihang Zhou, Xinwang Liu, Xifeng Guo, Zhiping Cai, En Zhu, and Jieren Cheng. "Deep Fusion Clustering Network." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9978–87. http://dx.doi.org/10.1609/aaai.v35i11.17198.

Full text

Abstract:

Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structure information for clustering performance enhancement. However, we observe that existing literature 1) lacks a dynamic fusion mechanism to selectively integrate and refine the information of graph structure and node attributes for consensus representation learning; 2) fails to extract information from both sides for robust target distribution (i.e., “groundtruth” soft labels) generation. To tackle the above issues, we propose a Deep Fusion Clustering Network (DFCN). Specifically, in our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder for consensus representation learning. Also, a reliable target distribution generation measure and a triplet self-supervision strategy, which facilitate cross-modality information exploitation, are designed for network training. Extensive experiments on six benchmark datasets have demonstrated that the proposed DFCN consistently outperforms the state-of-the-art deep clustering methods.

APA, Harvard, Vancouver, ISO, and other styles

3

Li, Xunkai, Youpeng Hu, Yaoqi Sun, Ji Hu, Jiyong Zhang, and Meixia Qu. "A Deep Graph Structured Clustering Network." IEEE Access 8 (2020): 161727–38. http://dx.doi.org/10.1109/access.2020.3020192.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Ma, Guixiang, Nesreen K. Ahmed, Theodore L. Willke, and Philip S. Yu. "Deep graph similarity learning: a survey." Data Mining and Knowledge Discovery 35, no. 3 (March 24, 2021): 688–725. http://dx.doi.org/10.1007/s10618-020-00733-5.

Full text

Abstract:

AbstractIn many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search. Recently, there has been an increasing interest in deep graph similarity learning, where the key idea is to learn a deep learning model that maps input graphs to a target space such that the distance in the target space approximates the structural distance in the input space. Here, we provide a comprehensive review of the existing literature of deep graph similarity learning. We propose a systematic taxonomy for the methods and applications. Finally, we discuss the challenges and future directions for this problem.

APA, Harvard, Vancouver, ISO, and other styles

5

Liao, Huifa, Jie Hu, Tianrui Li, Shengdong Du, and Bo Peng. "Deep linear graph attention model for attributed graph clustering." Knowledge-Based Systems 246 (June 2022): 108665. http://dx.doi.org/10.1016/j.knosys.2022.108665.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Liu, Yue, Wenxuan Tu, Sihang Zhou, Xinwang Liu, Linxuan Song, Xihong Yang, and En Zhu. "Deep Graph Clustering via Dual Correlation Reduction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7603–11. http://dx.doi.org/10.1609/aaai.v36i7.20726.

Full text

Abstract:

Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different groups, has attracted intensive attention in recent years. However, we observe that, in the process of node encoding, existing methods suffer from representation collapse which tends to map all data into the same representation. Consequently, the discriminative capability of the node representation is limited, leading to unsatisfied clustering performance. To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner. Specifically, in our method, we first design a siamese network to encode samples. Then by forcing the cross-view sample correlation matrix and cross-view feature correlation matrix to approximate two identity matrices, respectively, we reduce the information correlation in the dual-level, thus improving the discriminative capability of the resulting features. Moreover, in order to alleviate representation collapse caused by over-smoothing in GCN, we introduce a propagation regularization term to enable the network to gain long-distance information with the shallow network structure. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of the proposed DCRN against the existing state-of-the-art methods. The code of DCRN is available at https://github.com/yueliu1999/DCRN and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering on Github.

APA, Harvard, Vancouver, ISO, and other styles

7

Zhao, Yulin, Xunkai Li, Yinlin Zhu, Jin Li, Shuo Wang, and Bin Jiang. "A Scalable Deep Network for Graph Clustering via Personalized PageRank." Applied Sciences 12, no. 11 (May 29, 2022): 5502. http://dx.doi.org/10.3390/app12115502.

Full text

Abstract:

Recently, many models based on the combination of graph convolutional networks and deep learning have attracted extensive attention for their superior performance in graph clustering tasks. However, the existing models have the following limitations: (1) Existing models are limited by the calculation method of graph convolution, and their computational cost will increase exponentially as the graph scale grows. (2) Stacking too many convolutional layers causes the over-smoothing issue and neglects the local graph structure. (3) Expanding the range of the neighborhood and the model depth together is difficult due to the orthogonal relationship between them. Inspired by personalized pagerank and auto-encoder, we conduct the node-wise graph clustering task in the undirected simple graph as the research direction and propose a Scalable Deep Network (SDN) for graph clustering via personalized pagerank. Specifically, we utilize the combination of multi-layer perceptrons and linear propagation layer based on personalized pagerank as the backbone network (i.e., the Quasi-GNN module) and employ a DNN module for auto-encoder to learn different dimensions embeddings. After that, SDN combines the two embeddings correspondingly; then, it utilizes a dual self-supervised module to constrain the training of the embedding and clustering process. Our proposed Quasi-GNN module reduces the computational costs of traditional GNN models in a decoupled approach and solves the orthogonal relationship between the model depth and the neighborhood range. Meanwhile, it also alleviates the degraded clustering effect caused by the over-smoothing issue. We conducted experiments on five widely used graph datasets. The experimental results demonstrate that our model achieves state-of-the-art performance.

APA, Harvard, Vancouver, ISO, and other styles

8

Qi, Chao, Jianming Zhang, Hongjie Jia, Qirong Mao, Liangjun Wang, and Heping Song. "Deep face clustering using residual graph convolutional network." Knowledge-Based Systems 211 (January 2021): 106561. http://dx.doi.org/10.1016/j.knosys.2020.106561.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Qin, Shan, Ting Jiang, Sheng Wu, Ning Wang, and Xinran Zhao. "Graph Convolution-Based Deep Clustering for Speech Separation." IEEE Access 8 (2020): 82571–80. http://dx.doi.org/10.1109/access.2020.2989833.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Hu, Ruiqi, Shirui Pan, Guodong Long, Qinghua Lu, Liming Zhu, and Jing Jiang. "Going Deep: Graph Convolutional Ladder-Shape Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2838–45. http://dx.doi.org/10.1609/aaai.v34i03.5673.

Full text

Abstract:

Neighborhood aggregation algorithms like spectral graph convolutional networks (GCNs) formulate graph convolutions as a symmetric Laplacian smoothing operation to aggregate the feature information of one node with that of its neighbors. While they have achieved great success in semi-supervised node classification on graphs, current approaches suffer from the over-smoothing problem when the depth of the neural networks increases, which always leads to a noticeable degradation of performance. To solve this problem, we present graph convolutional ladder-shape networks (GCLN), a novel graph neural network architecture that transmits messages from shallow layers to deeper layers to overcome the over-smoothing problem and dramatically extend the scale of the neural networks with improved performance. We have validated the effectiveness of proposed GCLN at a node-wise level with a semi-supervised task (node classification) and an unsupervised task (node clustering), and at a graph-wise level with graph classification by applying a differentiable pooling operation. The proposed GCLN outperforms original GCNs, deep GCNs and other state-of-the-art GCN-based models for all three tasks, which were designed from various perspectives on six real-world benchmark data sets.

APA, Harvard, Vancouver, ISO, and other styles

11

Park, Jang You, Dong June Ryu, Kwang Woo Nam, Insung Jang, Minseok Jang, and Yonsik Lee. "DeepDBSCAN: Deep Density-Based Clustering for Geo-Tagged Photos." ISPRS International Journal of Geo-Information 10, no. 8 (August 14, 2021): 548. http://dx.doi.org/10.3390/ijgi10080548.

Full text

Abstract:

Density-based clustering algorithms have been the most commonly used algorithms for discovering regions and points of interest in cities using global positioning system (GPS) information in geo-tagged photos. However, users sometimes find more specific areas of interest using real objects captured in pictures. Recent advances in deep learning technology make it possible to recognize these objects in photos. However, since deep learning detection is a very time-consuming task, simply combining deep learning detection with density-based clustering is very costly. In this paper, we propose a novel algorithm supporting deep content and density-based clustering, called deep density-based spatial clustering of applications with noise (DeepDBSCAN). DeepDBSCAN incorporates object detection by deep learning into the density clustering algorithm using the nearest neighbor graph technique. Additionally, this supports a graph-based reduction algorithm that reduces the number of deep detections. We performed experiments with pictures shared by users on Flickr and compared the performance of multiple algorithms to demonstrate the excellence of the proposed algorithm.

APA, Harvard, Vancouver, ISO, and other styles

12

Forster, Richárd, and Agnes Fülöp. "Hierarchical clustering with deep Q-learning." Acta Universitatis Sapientiae, Informatica 10, no. 1 (August 1, 2018): 86–109. http://dx.doi.org/10.2478/ausi-2018-0006.

Full text

Abstract:

Abstract Following up on our previous study on applying hierarchical clustering algorithms to high energy particle physics, this paper explores the possibilities to use deep learning to generate models capable of processing the clusterization themselves. The technique chosen for training is reinforcement learning, that allows the system to evolve based on interactions between the model and the underlying graph. The result is a model, that by learning on a modest dataset of 10, 000 nodes during 70 epochs can reach 83, 77% precision for hierarchical and 86, 33% for high energy jet physics datasets in predicting the appropriate clusters.

APA, Harvard, Vancouver, ISO, and other styles

13

Jiang, Xiao, Pengjiang Qian, Yizhang Jiang, Yi Gu, and Aiguo Chen. "Deep self-supervised clustering with embedding adjacent graph features." Systems Science & Control Engineering 10, no. 1 (March 9, 2022): 336–46. http://dx.doi.org/10.1080/21642583.2022.2048321.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Ye, Xulun, and Jieyu Zhao. "Multi-manifold clustering: A graph-constrained deep nonparametric method." Pattern Recognition 93 (September 2019): 215–27. http://dx.doi.org/10.1016/j.patcog.2019.04.029.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Ding, Deqiong, Dan Zhuang, Xiaogao Yang, Xiao Zheng, and Chang Tang. "Latent Features Embedded Dynamic Graph Evolution Deep Clustering Network." Signal Processing 205 (April 2023): 108892. http://dx.doi.org/10.1016/j.sigpro.2022.108892.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Fu, Li Li, Yong Li Liu, and Li Jing Hao. "Research on Spectral Clustering." Applied Mechanics and Materials 687-691 (November 2014): 1350–53. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1350.

Full text

Abstract:

Spectral clustering algorithm is a kind of clustering algorithm based on spectral graph theory. As spectral clustering has deep theoretical foundation as well as the advantage in dealing with non-convex distribution, it has received much attention in machine learning and data mining areas. The algorithm is easy to implement, and outperforms traditional clustering algorithms such as K-means algorithm. This paper aims to give some intuitions on spectral clustering. We describe different graph partition criteria, the definition of spectral clustering, and clustering steps, etc. Finally, in order to solve the disadvantage of spectral clustering, some improvements are introduced briefly.

APA, Harvard, Vancouver, ISO, and other styles

17

Yu, Zhuohan, Yifu Lu, Yunhe Wang, Fan Tang, Ka-Chun Wong, and Xiangtao Li. "ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4671–79. http://dx.doi.org/10.1609/aaai.v36i4.20392.

Full text

Abstract:

Single-cell RNA sequencing (scRNA-seq) provides high-throughput information about the genome-wide gene expression levels at the single-cell resolution, bringing a precise understanding on the transcriptome of individual cells. Unfortunately, the rapidly growing scRNA-seq data and the prevalence of dropout events pose substantial challenges for cell type annotation. Here, we propose a single-cell model-based deep graph embedding clustering (scTAG) method, which simultaneously learns cell–cell topology representations and identifies cell clusters based on deep graph convolutional network. scTAG integrates the zero-inflated negative binomial (ZINB) model into a topology adaptive graph convolutional autoencoder to learn the low-dimensional latent representation and adopts Kullback–Leibler (KL) divergence for the clustering tasks. By simultaneously optimizing the clustering loss, ZINB loss, and the cell graph reconstruction loss, scTAG jointly optimizes cluster label assignment and feature learning with the topological structures preserved in an end-to-end manner. Extensive experiments on 16 single-cell RNA-seq datasets from diverse yet representative single-cell sequencing platforms demonstrate the superiority of scTAG over various state-of-the-art clustering methods.

APA, Harvard, Vancouver, ISO, and other styles

18

Makarov, Ilya, Dmitrii Kiselev, Nikita Nikitinsky, and Lovro Subelj. "Survey on graph embeddings and their applications to machine learning problems on graphs." PeerJ Computer Science 7 (February 4, 2021): e357. http://dx.doi.org/10.7717/peerj-cs.357.

Full text

Abstract:

Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

APA, Harvard, Vancouver, ISO, and other styles

19

Du, Hang-Yuan, and Wen-Jian Wang. "A Clustering Ensemble Framework with Integration of Data Characteristics and Structure Information: A Graph Neural Networks Approach." Mathematics 10, no. 11 (May 26, 2022): 1834. http://dx.doi.org/10.3390/math10111834.

Full text

Abstract:

Clustering ensemble is a research hotspot of data mining that aggregates several base clustering results to generate a single output clustering with improved robustness and stability. However, the validity of the ensemble result is usually affected by unreliability in the generation and integration of base clusterings. In order to address this issue, we develop a clustering ensemble framework viewed from graph neural networks that generates an ensemble result by integrating data characteristics and structure information. In this framework, we extract structure information from base clustering results of the data set by using a coupling affinity measure After that, we combine structure information with data characteristics by using a graph neural network (GNN) to learn their joint embeddings in latent space. Then, we employ a Gaussian mixture model (GMM) to predict the final cluster assignment in the latent space. Finally, we construct the GNN and GMM as a unified optimization model to integrate the objectives of graph embedding and consensus clustering. Our framework can not only elegantly combine information in feature space and structure space, but can also achieve suitable representations for final cluster partitioning. Thus, it can produce an outstanding result. Experimental results on six synthetic benchmark data sets and six real world data sets show that the proposed framework yields a better performance compared to 12 reference algorithms that are developed based on either clustering ensemble architecture or a deep clustering strategy.

APA, Harvard, Vancouver, ISO, and other styles

20

Li, Xiaocui, Hongzhi Yin, Ke Zhou, and Xiaofang Zhou. "Semi-supervised clustering with deep metric learning and graph embedding." World Wide Web 23, no. 2 (August 24, 2019): 781–98. http://dx.doi.org/10.1007/s11280-019-00723-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Ahmed, Muhammad Jamal, Faisal Saeed, Anand Paul, Sadeeq Jan, and Hyuncheol Seo. "A new affinity matrix weighted k-nearest neighbors graph to improve spectral clustering accuracy." PeerJ Computer Science 7 (September 6, 2021): e692. http://dx.doi.org/10.7717/peerj-cs.692.

Full text

Abstract:

Researchers have thought about clustering approaches that incorporate traditional clustering methods and deep learning techniques. These approaches normally boost the performance of clustering. Getting knowledge from large data-sets is quite an interesting task. In this case, we use some dimensionality reduction and clustering techniques. Spectral clustering is gaining popularity recently because of its performance. Lately, numerous techniques have been introduced to boost spectral clustering performance. One of the most significant part of these techniques is to construct a similarity graph. We introduced weighted k-nearest neighbors technique for the construction of similarity graph. Using this new metric for the construction of affinity matrix, we achieved good results as we tested it both on real and artificial data-sets.

APA, Harvard, Vancouver, ISO, and other styles

22

Manzo, Mario, and Alessandro Rozza. "DOPSIE: Deep-Order Proximity and Structural Information Embedding." Machine Learning and Knowledge Extraction 1, no. 2 (May 24, 2019): 684–97. http://dx.doi.org/10.3390/make1020040.

Full text

Abstract:

Graph-embedding algorithms map a graph into a vector space with the aim of preserving its structure and its intrinsic properties. Unfortunately, many of them are not able to encode the neighborhood information of the nodes well, especially from a topological prospective. To address this limitation, we propose a novel graph-embedding method called Deep-Order Proximity and Structural Information Embedding (DOPSIE). It provides topology and depth information at the same time through the analysis of the graph structure. Topological information is provided through clustering coefficients (CCs), which is connected to other structural properties, such as transitivity, density, characteristic path length, and efficiency, useful for representation in the vector space. The combination of individual node properties and neighborhood information constitutes an optimal network representation. Our experimental results show that DOPSIE outperforms state-of-the-art embedding methodologies in different classification problems.

APA, Harvard, Vancouver, ISO, and other styles

23

Hui, Binyuan, Pengfei Zhu, and Qinghua Hu. "Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4215–22. http://dx.doi.org/10.1609/aaai.v34i04.5843.

Full text

Abstract:

Graph convolutional networks (GCN) have achieved promising performance in attributed graph clustering and semi-supervised node classification because it is capable of modeling complex graphical structure, and jointly learning both features and relations of nodes. Inspired by the success of unsupervised learning in the training of deep models, we wonder whether graph-based unsupervised learning can collaboratively boost the performance of semi-supervised learning. In this paper, we propose a multi-task graph learning model, called collaborative graph convolutional networks (CGCN). CGCN is composed of an attributed graph clustering network and a semi-supervised node classification network. As Gaussian mixture models can effectively discover the inherent complex data distributions, a new end to end attributed graph clustering network is designed by combining variational graph auto-encoder with Gaussian mixture models (GMM-VGAE) rather than the classic k-means. If the pseudo-label of an unlabeled sample assigned by GMM-VGAE is consistent with the prediction of the semi-supervised GCN, it is selected to further boost the performance of semi-supervised learning with the help of the pseudo-labels. Extensive experiments on benchmark graph datasets validate the superiority of our proposed GMM-VGAE compared with the state-of-the-art attributed graph clustering networks. The performance of node classification is greatly improved by our proposed CGCN, which verifies graph-based unsupervised learning can be well exploited to enhance the performance of semi-supervised learning.

APA, Harvard, Vancouver, ISO, and other styles

24

Guo, Weiyu. "Sparse Dual Graph-Regularized Deep Nonnegative Matrix Factorization for Image Clustering." IEEE Access 9 (2021): 39926–38. http://dx.doi.org/10.1109/access.2021.3064631.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Chen, Junfen, Jie Han, Xiangjie Meng, Yan Li, and Haifeng Li. "Graph Convolutional Network Combined with Semantic Feature Guidance for Deep Clustering." Tsinghua Science and Technology 27, no. 5 (October 2022): 855–68. http://dx.doi.org/10.26599/tst.2021.9010066.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Li, Jianqiang, Guoxu Zhou, Yuning Qiu, Yanjiao Wang, Yu Zhang, and Shengli Xie. "Deep graph regularized non-negative matrix factorization for multi-view clustering." Neurocomputing 390 (May 2020): 108–16. http://dx.doi.org/10.1016/j.neucom.2019.12.054.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Frisoni, Giacomo, Gianluca Moro, Giulio Carlassare, and Antonella Carbonaro. "Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature." Sensors 22, no. 1 (December 21, 2021): 3. http://dx.doi.org/10.3390/s22010003.

Full text

Abstract:

The automatic extraction of biomedical events from the scientific literature has drawn keen interest in the last several years, recognizing complex and semantically rich graphical interactions otherwise buried in texts. However, very few works revolve around learning embeddings or similarity metrics for event graphs. This gap leaves biological relations unlinked and prevents the application of machine learning techniques to promote discoveries. Taking advantage of recent deep graph kernel solutions and pre-trained language models, we propose Deep Divergence Event Graph Kernels (DDEGK), an unsupervised inductive method to map events into low-dimensional vectors, preserving their structural and semantic similarities. Unlike most other systems, DDEGK operates at a graph level and does not require task-specific labels, feature engineering, or known correspondences between nodes. To this end, our solution compares events against a small set of anchor ones, trains cross-graph attention networks for drawing pairwise alignments (bolstering interpretability), and employs transformer-based models to encode continuous attributes. Extensive experiments have been done on nine biomedical datasets. We show that our learned event representations can be effectively employed in tasks such as graph classification, clustering, and visualization, also facilitating downstream semantic textual similarity. Empirical results demonstrate that DDEGK significantly outperforms other state-of-the-art methods.

APA, Harvard, Vancouver, ISO, and other styles

28

Song, Anping, Ruyi Ji, Wendong Qi, and Chenbei Zhang. "RGCLN: Relational Graph Convolutional Ladder-Shaped Networks for Signed Network Clustering." Applied Sciences 13, no. 3 (January 19, 2023): 1367. http://dx.doi.org/10.3390/app13031367.

Full text

Abstract:

Node embeddings are increasingly used in various analysis tasks of networks due to their excellent dimensional compression and feature representation capabilities. However, most researchers’ priorities have always been link prediction, which leads to signed network clustering being under-explored. Therefore, we propose an asymmetric ladder-shaped architecture called RGCLN based on multi-relational graph convolution that can fuse deep node features to generate node representations with great representational power. RGCLN adopts a deep framework to capture and convey information instead of using the common method in signed networks—balance theory. In addition, RGCLN adds a size constraint to the loss function to prevent image-like overfitting during the unsupervised learning process. Based on the node features learned by this end-to-end trained model, RGCLN performs community detection in a large number of real-world networks and generative networks, and the results indicate that our model has an advantage over state-of-the-art network embedding algorithms.

APA, Harvard, Vancouver, ISO, and other styles

29

Maddouri, Omar, Xiaoning Qian, and Byung-Jun Yoon. "Deep graph representations embed network information for robust disease marker identification." Bioinformatics 38, no. 4 (November 11, 2021): 1075–86. http://dx.doi.org/10.1093/bioinformatics/btab772.

Full text

Abstract:

Abstract Motivation Accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. Results By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility. Availability and implementation https://github.com/omarmaddouri/GCNCC. Supplementary information Supplementary data are available at Bioinformatics online.

APA, Harvard, Vancouver, ISO, and other styles

30

Belavin, V., E. Trofimova, and A. Ustyuzhanin. "Segmentation of EM showers for neutrino experiments with deep graph neural networks." Journal of Instrumentation 16, no. 12 (December 1, 2021): P12035. http://dx.doi.org/10.1088/1748-0221/16/12/p12035.

Full text

Abstract:

Abstract We introduce a first-ever algorithm for the reconstruction of multiple showers from the data collected with electromagnetic (EM) sampling calorimeters. Such detectors are widely used in High Energy Physics to measure the energy and kinematics of in-going particles. In this work, we consider the case when many electrons pass through an Emulsion Cloud Chamber (ECC) brick, initiating electron-induced electromagnetic showers, which can be the case with long exposure times or large input particle flux. For example, SHiP experiment is planning to use emulsion detectors for dark matter search and neutrino physics investigation. The expected full flux of SHiP experiment is about 1020 particles over five years. To reduce the cost of the experiment associated with the replacement of the ECC brick and off-line data taking (emulsion scanning), it is decided to increase exposure time. Thus, we expect to observe a lot of overlapping showers, which turn EM showers reconstruction into a challenging point cloud segmentation problem. Our reconstruction pipeline consists of a Graph Neural Network that predicts an adjacency matrix and a clustering algorithm. We propose a new layer type (EmulsionConv) that takes into account geometrical properties of shower development in ECC brick. For the clustering of overlapping showers, we use a modified hierarchical density-based clustering algorithm. Our method does not use any prior information about the incoming particles and identifies up to 87% of electromagnetic showers in emulsion detectors. The achieved energy resolution over 16,577 showers is σE/E = (0.095 ± 0.005) + (0.134 ± 0.011)/√(E). The main test bench for the algorithm for reconstructing electromagnetic showers is going to be SND@LHC.

APA, Harvard, Vancouver, ISO, and other styles

31

Ji, Junzhong, Ye Liang, and Minglong Lei. "Deep attributed graph clustering with self-separation regularization and parameter-free cluster estimation." Neural Networks 142 (October 2021): 522–33. http://dx.doi.org/10.1016/j.neunet.2021.07.012.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Jadhav, Pranavati Bajrang, and Vijaya Babu Burra. "Deep Learning in Social Networks for Overlappering Community Detection." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 12 (December 31, 2022): 35–43. http://dx.doi.org/10.17762/ijritcc.v10i12.5839.

Full text

Abstract:

The collection of nodes is termed as community in any network system that are tightly associated to the other nodes. In network investigation, identifying the community structure is crucial task, particularly for exposing connections between certain nodes. For community overlapping, network discovery, there are numerous methodologies described in the literature. Numerous scholars have recently focused on network embedding and feature learning techniques for node clustering. These techniques translate the network into a representation space with fewer dimensions. In this paper, a deep neural network-based model for learning graph representation and stacked auto-encoders are given a nonlinear embedding of the original graph to learn the model. In order to extract overlapping communities, an AEOCDSN algorithm is used. The efficiency of the suggested model is examined through experiments on real-world datasets of various sizes and accepted standards. The method outperforms various well-known community detection techniques, according to empirical findings.

APA, Harvard, Vancouver, ISO, and other styles

33

Gabriel, Nicholas, and Neil F. Johnson. "Using Neural Architectures to Model Complex Dynamical Systems." Advances in Artificial Intelligence and Machine Learning 02, no. 02 (2022): 366–84. http://dx.doi.org/10.54364/aaiml.2022.1124.

Full text

Abstract:

The natural, physical and social worlds abound with feedback processes that make the challenge of modeling the underlying system an extremely complex one. This paper proposes an end-to-end deep learning approach to modelling such so-called complex systems which addresses two problems: (1) scientific model discovery when we have only incomplete/partial knowledge of system dynamics; (2) integration of graph-structured data into scientific machine learning (SciML) using graph neural networks. It is well known that deep learning (DL) has had remarkable successin leveraging large amounts of unstructured data into downstream tasks such as clustering, classification, and regression. Recently, the development of graph neural networks has extended DL techniques to graph structured data of complex systems. However, DL methods still appear largely disjointed with established scientific knowledge, and the contribution to basic science is not always apparent. This disconnect has spurred the development of physics-informed deep learning, and more generally, the emerging discipline of SciML. Modelling complex systems in the physical, biological, and social sciences within the SciML framework requires further considerations. We argue the need to consider heterogeneous, graph-structured data as well as the effective scale at which we can observe system dynamics. Our proposal would open up a joint approach to the previously distinct fields of graph representation learning and SciML.

APA, Harvard, Vancouver, ISO, and other styles

34

Zhang, Tao, Yang Cong, Gan Sun, Qianqian Wang, and Zhenming Ding. "Visual Tactile Fusion Object Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (April 3, 2020): 10426–33. http://dx.doi.org/10.1609/aaai.v34i06.6612.

Full text

Abstract:

Object clustering, aiming at grouping similar objects into one cluster with an unsupervised strategy, has been extensively-studied among various data-driven applications. However, most existing state-of-the-art object clustering methods (e.g., single-view or multi-view clustering methods) only explore visual information, while ignoring one of most important sensing modalities, i.e., tactile information which can help capture different object properties and further boost the performance of object clustering task. To effectively benefit both visual and tactile modalities for object clustering, in this paper, we propose a deep Auto-Encoder-like Non-negative Matrix Factorization framework for visual-tactile fusion clustering. Specifically, deep matrix factorization constrained by an under-complete Auto-Encoder-like architecture is employed to jointly learn hierarchical expression of visual-tactile fusion data, and preserve the local structure of data generating distribution of visual and tactile modalities. Meanwhile, a graph regularizer is introduced to capture the intrinsic relations of data samples within each modality. Furthermore, we propose a modality-level consensus regularizer to effectively align the visual and tactile data in a common subspace in which the gap between visual and tactile data is mitigated. For the model optimization, we present an efficient alternating minimization strategy to solve our proposed model. Finally, we conduct extensive experiments on public datasets to verify the effectiveness of our framework.

APA, Harvard, Vancouver, ISO, and other styles

35

Du, Guowang, Lihua Zhou, Kevin Lü, and Haiyan Ding. "Deep multiple non-negative matrix factorization for multi-view clustering." Intelligent Data Analysis 25, no. 2 (March 4, 2021): 339–57. http://dx.doi.org/10.3233/ida-195075.

Full text

Abstract:

Multi-view clustering aims to group similar samples into the same clusters and dissimilar samples into different clusters by integrating heterogeneous information from multi-view data. Non-negative matrix factorization (NMF) has been widely applied to multi-view clustering owing to its interpretability. However, most NMF-based algorithms only factorize multi-view data based on the shallow structure, neglecting complex hierarchical and heterogeneous information in multi-view data. In this paper, we propose a deep multiple non-negative matrix factorization (DMNMF) framework based on AutoEncoder for multi-view clustering. DMNMF consists of multiple Encoder Components and Decoder Components with deep structures. Each pair of Encoder Component and Decoder Component are used to hierarchically factorize the input data from a view for capturing the hierarchical information, and all Encoder and Decoder Components are integrated into an abstract level to learn a common low-dimensional representation for combining the heterogeneous information across multi-view data. Furthermore, graph regularizers are also introduced to preserve the local geometric information of each view. To optimize the proposed framework, an iterative updating scheme is developed. Besides, the corresponding algorithm called MVC-DMNMF is also proposed and implemented. Extensive experiments on six benchmark datasets have been conducted, and the experimental results demonstrate the superior performance of our proposed MVC-DMNMF for multi-view clustering compared to other baseline algorithms.

APA, Harvard, Vancouver, ISO, and other styles

36

Kong, Xiangjie, Jiaxing Li, Luna Wang, Guojiang Shen, Yiming Sun, and Ivan Lee. "Recurrent-DC: A deep representation clustering model for university profiling based on academic graph." Future Generation Computer Systems 116 (March 2021): 156–67. http://dx.doi.org/10.1016/j.future.2020.10.019.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Buterez, David, Ioana Bica, Ifrah Tariq, Helena Andrés-Terré, and Pietro Liò. "CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks." Bioinformatics 38, no. 5 (December 2, 2021): 1277–86. http://dx.doi.org/10.1093/bioinformatics/btab804.

Full text

Abstract:

Abstract Motivation Single-cell RNA sequencing allows high-resolution views of individual cells for libraries of up to millions of samples, thus motivating the use of deep learning for analysis. In this study, we introduce the use of graph neural networks for the unsupervised exploration of scRNA-seq data by developing a variational graph autoencoder architecture with graph attention layers that operates directly on the connectivity between cells, focusing on dimensionality reduction and clustering. With the help of several case studies, we show that our model, named CellVGAE, can be effectively used for exploratory analysis even on challenging datasets, by extracting meaningful features from the data and providing the means to visualize and interpret different aspects of the model. Results We show that CellVGAE is more interpretable than existing scRNA-seq variational architectures by analysing the graph attention coefficients. By drawing parallels with other scRNA-seq studies on interpretability, we assess the validity of the relationships modelled by attention, and furthermore, we show that CellVGAE can intrinsically capture information such as pseudotime and NF-ĸB activation dynamics, the latter being a property that is not generally shared by existing neural alternatives. We then evaluate the dimensionality reduction and clustering performance on 9 difficult and well-annotated datasets by comparing with three leading neural and non-neural techniques, concluding that CellVGAE outperforms competing methods. Finally, we report a decrease in training times of up to × 20 on a dataset of 1.3 million cells compared to existing deep learning architectures. Availabilityand implementation The CellVGAE code is available at https://github.com/davidbuterez/CellVGAE. Supplementary information Supplementary data are available at Bioinformatics online.

APA, Harvard, Vancouver, ISO, and other styles

38

Chen, Dongming, Mingshuo Nie, Jie Wang, Yun Kong, Dongqi Wang, and Xinyu Huang. "Community Detection Based on Graph Representation Learning in Evolutionary Networks." Applied Sciences 11, no. 10 (May 14, 2021): 4497. http://dx.doi.org/10.3390/app11104497.

Full text

Abstract:

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

APA, Harvard, Vancouver, ISO, and other styles

39

Villanueva-Domingo, Pablo, and Francisco Villaescusa-Navarro. "Learning Cosmology and Clustering with Cosmic Graphs." Astrophysical Journal 937, no. 2 (October 1, 2022): 115. http://dx.doi.org/10.3847/1538-4357/ac8930.

Full text

Abstract:

Abstract We train deep-learning models on thousands of galaxy catalogs from the state-of-the-art hydrodynamic simulations of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to work with irregular and sparse data, like the distribution of galaxies in the universe. We first show that GNNs can learn to compute the power spectrum of galaxy catalogs with a few percent accuracy. We then train GNNs to perform likelihood-free inference at the galaxy-field level. Our models are able to infer the value of Ωm with a ∼12%–13% accuracy just from the positions of ∼1000 galaxies in a volume of ( 25 h − 1 Mpc ) 3 at z = 0 while accounting for astrophysical uncertainties as modeled in CAMELS. Incorporating information from galaxy properties, such as the stellar mass, stellar metallicity, and stellar radius, increases the accuracy to 4%–8%. Our models are built to be translation and rotation invariant, and they can extract information from any scale larger than the minimum distance between two galaxies. However, our models are not completely robust: testing on simulations run with a different subgrid physics than the ones used for training does not yield accurate results.

APA, Harvard, Vancouver, ISO, and other styles

40

Shin, Yong-Min, Sun-Woo Kim, Eun-Bi Yoon, and Won-Yong Shin. "Prototype-Based Explanations for Graph Neural Networks (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 13047–48. http://dx.doi.org/10.1609/aaai.v36i11.21660.

Full text

Abstract:

Aside the high performance of graph neural networks (GNNs), considerable attention has recently been paid to explanations of black-box deep learning models. Unlike most studies focusing on model explanations based on a specific graph instance, we propose Prototype-bAsed GNN-Explainer (PAGE), a novel model-level explanation method for graph-level classification that explains what the underlying model has learned by providing human-interpretable prototypes. Specifically, our method performs clustering on the embedding space of the underlying GNN model; extracts embeddings in each cluster; and discovers prototypes, which serve as model explanations, by estimating the maximum common subgraph (MCS) from the extracted embeddings. Experimental evaluation demonstrates that PAGE not only provides high-quality explanations but also outperforms the state-of-the-art model-level method in terms of consistency and faithfulness that are performance metrics for quantitative evaluations.

APA, Harvard, Vancouver, ISO, and other styles

41

Huang, Yixin, Zhongcheng Mu, Shufan Wu, Benjie Cui, and Yuxiao Duan. "Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning." Remote Sensing 13, no. 12 (June 18, 2021): 2377. http://dx.doi.org/10.3390/rs13122377.

Full text

Abstract:

Earth observation satellite task scheduling research plays a key role in space-based remote sensing services. An effective task scheduling strategy can maximize the utilization of satellite resources and obtain larger objective observation profits. In this paper, inspired by the success of deep reinforcement learning in optimization domains, the deep deterministic policy gradient algorithm is adopted to solve a time-continuous satellite task scheduling problem. Moreover, an improved graph-based minimum clique partition algorithm is proposed for preprocessing in the task clustering phase by considering the maximum task priority and the minimum observation slewing angle under constraint conditions. Experimental simulation results demonstrate that the deep reinforcement learning-based task scheduling method is feasible and performs much better than traditional metaheuristic optimization algorithms, especially in large-scale problems.

APA, Harvard, Vancouver, ISO, and other styles

42

Romero, Luis, Joaquim Blesa, Vicenç Puig, Gabriela Cembrano, and Carlos Trapiello. "First Results in Leak Localization in Water Distribution Networks using Graph-Based Clustering and Deep Learning." IFAC-PapersOnLine 53, no. 2 (2020): 16691–96. http://dx.doi.org/10.1016/j.ifacol.2020.12.1104.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Zhao, Yang, Yuan Yuan, and Qi Wang. "Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification." Remote Sensing 11, no. 4 (February 15, 2019): 399. http://dx.doi.org/10.3390/rs11040399.

Full text

Abstract:

Hyperspectral image classification is a challenging and significant domain in the field of remote sensing with numerous applications in agriculture, environmental science, mineralogy, and surveillance. In the past years, a growing number of advanced hyperspectral remote sensing image classification techniques based on manifold learning, sparse representation and deep learning have been proposed and reported a good performance in accuracy and efficiency on state-of-the-art public datasets. However, most existing methods still face challenges in dealing with large-scale hyperspectral image datasets due to their high computational complexity. In this work, we propose an improved spectral clustering method for large-scale hyperspectral image classification without any prior information. The proposed algorithm introduces two efficient approximation techniques based on Nyström extension and anchor-based graph to construct the affinity matrix. We also propose an effective solution to solve the eigenvalue decomposition problem by multiplicative update optimization. Experiments on both the synthetic datasets and the hyperspectral image datasets were conducted to demonstrate the efficiency and effectiveness of the proposed algorithm.

APA, Harvard, Vancouver, ISO, and other styles

44

Cheng, Lijun, Pratik Karkhanis, Birkan Gokbag, Yueze Liu, and Lang Li. "DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data." PLOS Computational Biology 18, no. 4 (April 11, 2022): e1008885. http://dx.doi.org/10.1371/journal.pcbi.1008885.

Full text

Abstract:

Single-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use, and no algorithms to identify both calibration and new cell populations has been well established. A deep learning with graphic cluster (DGCyTOF) visualization is developed as a new integrated embedding visualization approach in identifying canonical and new cell types. The DGCyTOF combines deep-learning classification and hierarchical stable-clustering methods to sequentially build a tri-layer construct for known cell types and the identification of new cell types. First, deep classification learning is constructed to distinguish calibration cell populations from all cells by softmax classification assignment under a probability threshold, and graph embedding clustering is then used to identify new cell populations sequentially. In the middle of two-layer, cell labels are automatically adjusted between new and unknown cell populations via a feedback loop using an iteration calibration system to reduce the rate of error in the identification of cell types, and a 3-dimensional (3D) visualization platform is finally developed to display the cell clusters with all cell-population types annotated. Utilizing two benchmark CyTOF databases comprising up to 43 million cells, we compared accuracy and speed in the identification of cell types among DGCyTOF, DeepCyTOF, and other technologies including dimension reduction with clustering, including Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), Isometric Feature Mapping (Isomap), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) with k-means clustering and Gaussian mixture clustering. We observed the DGCyTOF represents a robust complete learning system with high accuracy, speed and visualization by eight measurement criteria. The DGCyTOF displayed F-scores of 0.9921 for CyTOF1 and 0.9992 for CyTOF2 datasets, whereas those scores were only 0.507 and 0.529 for the t-SNE+k-means; 0.565 and 0.59, for UMAP+ k-means. Comparison of DGCyTOF with t-SNE and UMAP visualization in accuracy demonstrated its approximately 35% superiority in predicting cell types. In addition, observation of cell-population distribution was more intuitive in the 3D visualization in DGCyTOF than t-SNE and UMAP visualization. The DGCyTOF model can automatically assign known labels to single cells with high accuracy using deep-learning classification assembling with traditional graph-clustering and dimension-reduction strategies. Guided by a calibration system, the model seeks optimal accuracy balance among calibration cell populations and unknown cell types, yielding a complete and robust learning system that is highly accurate in the identification of cell populations compared to results using other methods in the analysis of single-cell CyTOF data. Application of the DGCyTOF method to identify cell populations could be extended to the analysis of single-cell RNASeq data and other omics data.

APA, Harvard, Vancouver, ISO, and other styles

45

Liu, Hao, Langzhou He, Fan Zhang, Zhen Wang, and Chao Gao. "Dynamic community detection over evolving networks based on the optimized deep graph infomax." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 5 (May 2022): 053119. http://dx.doi.org/10.1063/5.0086795.

Full text

Abstract:

As complex systems, dynamic networks have obvious nonlinear features. Detecting communities in dynamic networks is of great importance for understanding the functions of networks and mining evolving relationships. Recently, some network embedding-based methods stand out by embedding the global network structure and properties into a low-dimensional representation for community detection. However, such kinds of methods can only be utilized at each single time step independently. As a consequence, the information of all time steps requires to be stored, which increases the computational cost. Besides this, the neighbors of target nodes are considered equally when aggregating nodes in networks, which omits the local structural feature of networks and influences the accuracy of node representation. To overcome such shortcomings, this paper proposes a novel optimized dynamic deep graph infomax (ODDGI) method for dynamic community detection. Since the recurrent neural network (RNN) can capture the dynamism of networks while avoiding storing all information of dynamic networks, our ODDGI utilizes RNN to update deep graph infomax parameters, and thus, there is no need to store the knowledge of nodes in full time span anymore. Moreover, the importance of nodes is considered using similarity aggregation strategy to improve the accuracy of node representation. The experimental results on both the real-world and synthetic networks prove that our method surpasses other state-of-the-art dynamic community detection algorithms in clustering accuracy and stability.

APA, Harvard, Vancouver, ISO, and other styles

46

Spyridis, Yannis, Thomas Lagkas, Panagiotis Sarigiannidis, Vasileios Argyriou, Antonios Sarigiannidis, George Eleftherakis, and Jie Zhang. "Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks." Sensors 21, no. 11 (June 7, 2021): 3936. http://dx.doi.org/10.3390/s21113936.

Full text

Abstract:

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.

APA, Harvard, Vancouver, ISO, and other styles

47

Jin, S., C. Jing, Y. Wang, and X. Lv. "SPATIOTEMPORAL GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR METRO FLOW PREDICTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 2, 2022): 403–9. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-403-2022.

Full text

Abstract:

Abstract. Forecasting urban metro flow accurately plays an important role for station management and passenger safety. Owing to the limitations of non-linearity and complexity of traffic flow data, traditional methods cannot satisfy the requirements of effectively capturing spatiotemporal dependencies at the metro network level, which makes it difficult to demonstrate high performance. In this paper, a novel deep learning method is proposed based on Graph Neural Networks (GNN), named STGCN-Metro (SpatioTemporal Graph Convolutional Network based on Metro network), to forecast the short-term inflow and outflow volumes of metro passengers. The proposed model is composed of two spatiotemporal convolutional blocks, which is integrated with the Dilated Convolutional Neural Network (DCNN) and Cluster-Graph Convolutional Network (Cluster-GCN). The DCNN is employed with different dilation rates to capture temporal dependence in larger receptive field. In addition, compare with GCN, the Cluster-GCN is applied the graph clustering algorithms to reduce computational resources considering spatial heterogeneity. A real-world dataset collected in Shanghai metro stations is conducted for validation, and the results demonstrate that the proposed model achieves higher performance, outperforming some well-known baseline models.

APA, Harvard, Vancouver, ISO, and other styles

48

Peng, Sen, Jing Cheng, Xingqi Wu, Xu Fang, and Qing Wu. "Pressure Sensor Placement in Water Supply Network Based on Graph Neural Network Clustering Method." Water 14, no. 2 (January 7, 2022): 150. http://dx.doi.org/10.3390/w14020150.

Full text

Abstract:

Pressure sensor placement is critical to system safety and operation optimization of water supply networks (WSNs). The majority of existing studies focuses on sensitivity or burst identification ability of monitoring systems based on certain specific operating conditions of WSNs, while nodal connectivity or long-term hydraulic fluctuation is not fully considered and analyzed. A new method of pressure sensor placement is proposed in this paper based on Graph Neural Networks. The method mainly consists of two steps: monitoring partition establishment and sensor placement. (1) Structural Deep Clustering Network algorithm is used for clustering analysis with the integration of complicated topological and hydraulic characteristics, and a WSN is divided into several monitoring partitions. (2) Then, sensor placement is carried out based on burst identification analysis, a quantitative metric named “indicator tensor” is developed to calculate hydraulic characteristics in time series, and the node with the maximum average partition perception rate is selected as the sensor in each monitoring partition. The results showed that the proposed method achieved a better monitoring scheme with more balanced distribution of sensors and higher coverage rate for pipe burst detection. This paper offers a new robust framework, which can be easily applied in the decision-making process of monitoring system establishment.

APA, Harvard, Vancouver, ISO, and other styles

49

Budisteanu, Elena-Alexandra, and Irina Georgiana Mocanu. "Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition." Sensors 21, no. 18 (September 21, 2021): 6309. http://dx.doi.org/10.3390/s21186309.

Full text

Abstract:

Human activity recognition is an extensively researched topic in the last decade. Recent methods employ supervised and unsupervised deep learning techniques in which spatial and temporal dependency is modeled. This paper proposes a novel approach for human activity recognition using skeleton data. The method combines supervised and unsupervised learning algorithms in order to provide qualitative results and performance in real time. The proposed method involves a two-stage framework: the first stage applies an unsupervised clustering technique to group up activities based on their similarity, while the second stage classifies data assigned to each group using graph convolutional networks. Different clustering techniques and data augmentation strategies are explored for improving the training process. The results were compared against the state of the art methods and the proposed model achieved 90.22% Top-1 accuracy performance for NTU-RGB+D dataset (the performance was increased by approximately 9% compared with the baseline graph convolutional method). Moreover, inference time and total number of parameters stay within the same magnitude order. Extending the initial set of activities with additional classes is fast and robust, since there is no required retraining of the entire architecture but only to retrain the cluster to which the activity is assigned.

APA, Harvard, Vancouver, ISO, and other styles

50

Sun, Zhonglin, Yannis Spyridis, Thomas Lagkas, Achilleas Sesis, Georgios Efstathopoulos, and Panagiotis Sarigiannidis. "End-to-End Deep Graph Convolutional Neural Network Approach for Intentional Islanding in Power Systems Considering Load-Generation Balance." Sensors 21, no. 5 (February 27, 2021): 1650. http://dx.doi.org/10.3390/s21051650.

Full text

Abstract:

Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper proposes a deep learning method to solve the problem of intentional islanding in an end-to-end manner. Two types of loss functions are examined for the graph partitioning task, and a loss function is added on the deep learning model, aiming to minimise the load-generation imbalance in the formed islands. In addition, the proposed solution incorporates a technique for merging the independent buses to their nearest neighbour in case there are isolated buses after the clusterisation, improving the final result in cases of large and complex systems. Several experiments demonstrate that the introduced deep learning method provides effective clustering results for intentional islanding, managing to keep the power imbalance low and creating stable islands. Finally, the proposed method is dynamic, relying on real-time system conditions to calculate the result.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Deep graph clustering'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles