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Journal articles on the topic 'High-dimensional sparse graph'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Zou, Yuanhang, Zhihao Ding, Jieming Shi, Shuting Guo, Chunchen Su, and Yafei Zhang. "EmbedX: A Versatile, Efficient and Scalable Platform to Embed Both Graphs and High-Dimensional Sparse Data." Proceedings of the VLDB Endowment 16, no. 12 (2023): 3543–56. http://dx.doi.org/10.14778/3611540.3611546.

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In modern online services, it is of growing importance to process web-scale graph data and high-dimensional sparse data together into embeddings for downstream tasks, such as recommendation, advertisement, prediction, and classification. There exist learning methods and systems for either high-dimensional sparse data or graphs, but not both. There is an urgent need in industry to have a system to efficiently process both types of data for higher business value, which however, is challenging. The data in Tencent contains billions of samples with sparse features in very high dimensions, and grap

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Xie, Anze, Anders Carlsson, Jason Mohoney, et al. "Demo of marius." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2759–62. http://dx.doi.org/10.14778/3476311.3476338.

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Graph embeddings have emerged as the de facto representation for modern machine learning over graph data structures. The goal of graph embedding models is to convert high-dimensional sparse graphs into low-dimensional, dense and continuous vector spaces that preserve the graph structure properties. However, learning a graph embedding model is a resource intensive process, and existing solutions rely on expensive distributed computation to scale training to instances that do not fit in GPU memory. This demonstration showcases Marius: a new open-source engine for learning graph embedding models

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Liu, Jianyu, Guan Yu, and Yufeng Liu. "Graph-based sparse linear discriminant analysis for high-dimensional classification." Journal of Multivariate Analysis 171 (May 2019): 250–69. http://dx.doi.org/10.1016/j.jmva.2018.12.007.

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Wang, Li-e., and Xianxian Li. "A Clustering-Based Bipartite Graph Privacy-Preserving Approach for Sharing High-Dimensional Data." International Journal of Software Engineering and Knowledge Engineering 24, no. 07 (2014): 1091–111. http://dx.doi.org/10.1142/s0218194014500363.

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Driven by mutual benefits, there is a demand for transactional data sharing among organizations or parties for research or business analysis purpose. It becomes an essential concern to provide privacy-preserving data sharing and meanwhile maintain data utility, due to the fact that transactional data may contain sensitive personal information. Existing privacy-preserving methods, such as k-anonymity and l-diversity, cannot handle high-dimensional sparse data well, since they would bring about much data distortion in the anonymization process. In this paper, we use bipartite graphs with node at

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Ni, Li, Peng Manman, and Wu Qiang. "A Spectral Clustering Algorithm for Non-Linear Graph Embedding in Information Networks." Applied Sciences 14, no. 11 (2024): 4946. http://dx.doi.org/10.3390/app14114946.

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With the development of network technology, information networks have become one of the most important means for people to understand society. As the scale of information networks expands, the construction of network graphs and high-dimensional feature representation will become major factors affecting the performance of spectral clustering algorithms. To address this issue, in this paper, we propose a spectral clustering algorithm based on similarity graphs and non-linear deep embedding, named SEG_SC.This algorithm introduces a new spectral clustering model that explores the underlying struct

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Saul, Lawrence K. "A tractable latent variable model for nonlinear dimensionality reduction." Proceedings of the National Academy of Sciences 117, no. 27 (2020): 15403–8. http://dx.doi.org/10.1073/pnas.1916012117.

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We propose a latent variable model to discover faithful low-dimensional representations of high-dimensional data. The model computes a low-dimensional embedding that aims to preserve neighborhood relationships encoded by a sparse graph. The model both leverages and extends current leading approaches to this problem. Like t-distributed Stochastic Neighborhood Embedding, the model can produce two- and three-dimensional embeddings for visualization, but it can also learn higher-dimensional embeddings for other uses. Like LargeVis and Uniform Manifold Approximation and Projection, the model produc

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Li, Xinyu, Xiaoguang Gao, and Chenfeng Wang. "A Novel BN Learning Algorithm Based on Block Learning Strategy." Sensors 20, no. 21 (2020): 6357. http://dx.doi.org/10.3390/s20216357.

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Learning accurate Bayesian Network (BN) structures of high-dimensional and sparse data is difficult because of high computation complexity. To learn the accurate structure for high-dimensional and sparse data faster, this paper adopts a divide and conquer strategy and proposes a block learning algorithm with a mutual information based K-means algorithm (BLMKM algorithm). This method utilizes an improved K-means algorithm to block the nodes in BN and a maximum minimum parents and children (MMPC) algorithm to obtain the whole skeleton of BN and find possible graph structures based on separated b

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Dobson, Andrew, and Kostas Bekris. "Improved Heuristic Search for Sparse Motion Planning Data Structures." Proceedings of the International Symposium on Combinatorial Search 5, no. 1 (2021): 196–97. http://dx.doi.org/10.1609/socs.v5i1.18334.

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Sampling-based methods provide efficient, flexible solutions for motion planning, even for complex, high-dimensional systems. Asymptotically optimal planners ensure convergence to the optimal solution, but produce dense structures. This work shows how to extend sparse methods achieving asymptotic near-optimality using multiple-goal heuristic search during graph constuction. The resulting method produces identical output to the existing Incremental Roadmap Spanner approach but in an order of magnitude less time.

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Li, Peng, Mosharaf Md Parvej, Chenghao Zhang, Shufang Guo, and Jing Zhang. "Advances in the Development of Representation Learning and Its Innovations against COVID-19." COVID 3, no. 9 (2023): 1389–415. http://dx.doi.org/10.3390/covid3090096.

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In bioinformatics research, traditional machine-learning methods have demonstrated efficacy in addressing Euclidean data. However, real-world data often encompass non-Euclidean forms, such as graph data, which contain intricate structural patterns or high-order relationships that elude conventional machine-learning approaches. Representation learning seeks to derive valuable data representations from enhancing predictive or analytic tasks, capturing vital patterns and structures. This method has proven particularly beneficial in bioinformatics and biomedicine, as it effectively handles high-di

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Li, Ying, Xiaojun Xu, and Jianbo Li. "High-Dimensional Sparse Graph Estimation by Integrating DTW-D Into Bayesian Gaussian Graphical Models." IEEE Access 6 (2018): 34279–87. http://dx.doi.org/10.1109/access.2018.2849213.

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Merkel, Nikolai, Pierre Toussing, Ruben Mayer, and Hans-Arno Jacobsen. "Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study." Proceedings of the VLDB Endowment 18, no. 2 (2024): 293–307. https://doi.org/10.14778/3705829.3705846.

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Graph neural networks (GNNs) are a type of neural network capable of learning on graph-structured data. However, training GNNs on large-scale graphs is challenging due to iterative aggregations of high-dimensional features from neighboring vertices within sparse graph structures combined with neural network operations. The sparsity of graphs frequently results in suboptimal memory access patterns and longer training time. Graph reordering is an optimization strategy aiming to improve the graph data layout. It has shown to be effective to speed up graph analytics workloads, but its effect on th

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Kefato, Zekarias, and Sarunas Girdzijauskas. "Gossip and Attend: Context-Sensitive Graph Representation Learning." Proceedings of the International AAAI Conference on Web and Social Media 14 (May 26, 2020): 351–59. http://dx.doi.org/10.1609/icwsm.v14i1.7305.

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Graph representation learning (GRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and often sparse graphs. Most studies explore the structure and metadata associated with the graph using random walks and employ an unsupervised or semi-supervised learning schemes. Learning in these methods is context-free, resulting in only a single representation per node. Recently studies have argued on the adequacy of a single representation and proposed context-sensitive approaches, which are capable of extracting multiple node representations for different c

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Li, Pei Heng, Taeho Lee, and Hee Yong Youn. "Dimensionality Reduction with Sparse Locality for Principal Component Analysis." Mathematical Problems in Engineering 2020 (May 20, 2020): 1–12. http://dx.doi.org/10.1155/2020/9723279.

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Various dimensionality reduction (DR) schemes have been developed for projecting high-dimensional data into low-dimensional representation. The existing schemes usually preserve either only the global structure or local structure of the original data, but not both. To resolve this issue, a scheme called sparse locality for principal component analysis (SLPCA) is proposed. In order to effectively consider the trade-off between the complexity and efficiency, a robust L2,p-norm-based principal component analysis (R2P-PCA) is introduced for global DR, while sparse representation-based locality pre

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Zhang, Tianjiao, Jixiang Ren, Liangyu Li, et al. "scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering." International Journal of Molecular Sciences 25, no. 11 (2024): 5976. http://dx.doi.org/10.3390/ijms25115976.

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Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and significant sparsity, presenting considerable challenges for clustering. In the high-dimensional gene expression space, cells may form complex topological structures. Many conventional scRNA-seq data analysis methods focus on identifying cell subgroups rather than exploring these potential high-dimen

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Chen, Dongming, Mingshuo Nie, Hupo Zhang, Zhen Wang, and Dongqi Wang. "Network Embedding Algorithm Taking in Variational Graph AutoEncoder." Mathematics 10, no. 3 (2022): 485. http://dx.doi.org/10.3390/math10030485.

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Complex networks with node attribute information are employed to represent complex relationships between objects. Research of attributed network embedding fuses the topology and the node attribute information of the attributed network in the common latent representation space, to encode the high-dimensional sparse network information to the low-dimensional dense vector representation, effectively improving the performance of the network analysis tasks. The current research on attributed network embedding is presently facing problems of high-dimensional sparsity of attribute eigenmatrix and und

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Chen, Pingfei, Xuyang Li, Yong Peng, Xiangsuo Fan, and Qi Li. "WSSGCN: Hyperspectral Forest Image Classification via Watershed Superpixel Segmentation and Sparse Graph Convolutional Networks." Forests 16, no. 5 (2025): 827. https://doi.org/10.3390/f16050827.

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Hyperspectral image classification is crucial in remote sensing but faces challenges in forest ecosystem studies due to high-dimensional data, spectral variability, and spatial heterogeneity. Watershed Superpixel Segmentation and Sparse Graph Convolutional Networks (WSSGCN), a novel framework designed for efficient forest image classification, is introduced in this paper. Watershed superpixel segmentation is first used by the method to divide hyperspectral images into semantically consistent regions, reducing computational complexity while preserving terrain boundary information. On this basis

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Cai, Jun, Xin Xu, Hongpeng Zhu, and Jian Cheng. "An Efficient Compressive Sensing Event-Detection Scheme for Internet of Things System Based on Sparse-Graph Codes." Sensors 23, no. 10 (2023): 4620. http://dx.doi.org/10.3390/s23104620.

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This work studied the event-detection problem in an Internet of Things (IoT) system, where a group of sensor nodes are placed in the region of interest to capture sparse active event sources. Using compressive sensing (CS), the event-detection problem is modeled as recovering the high-dimensional integer-valued sparse signal from incomplete linear measurements. We show that the sensing process in IoT system produces an equivalent integer CS using sparse graph codes at the sink node, for which one can devise a simple deterministic construction of a sparse measurement matrix and an efficient int

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Ye, Nanjun. "A Penetrative Multidimensional Data Analytics Model for Complex Relationship Mining over Knowledge Graphs." Journal of Computing and Electronic Information Management 17, no. 2 (2025): 34–41. https://doi.org/10.54097/87rgwp44.

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This study proposes a deep multidimensional data analytics framework for extracting intricate relationships from knowledge graphs, which tackles the challenge of discovering hidden connections in heterogeneous and high-dimensional datasets. The proposed method unifies three principal elements: Dynamic Meta-Path Penetration, Nested Subgraph Extraction, and Tensor-Graph Fusion, which together permit a structured investigation of hidden connections. Dynamic Meta-Path Penetration applies reinforcement learning to traverse the graph, directed by a reward system prioritizing informative routes. Nest

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Yang, Qian, Jiaming Zhang, Junjie Zhang, et al. "Graph Transformer Network Incorporating Sparse Representation for Multivariate Time Series Anomaly Detection." Electronics 13, no. 11 (2024): 2032. http://dx.doi.org/10.3390/electronics13112032.

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Cyber–physical systems (CPSs) serve as the pivotal core of Internet of Things (IoT) infrastructures, such as smart grids and intelligent transportation, deploying interconnected sensing devices to monitor operating status. With increasing decentralization, the surge in sensor devices expands the potential vulnerability to cyber attacks. It is imperative to conduct anomaly detection research on the multivariate time series data that these sensors produce to bolster the security of distributed CPSs. However, the high dimensionality, absence of anomaly labels in real-world datasets, and intricate

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Li, Shuang, Bing Liu, and Chen Zhang. "Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing." Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/4920670.

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Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method

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Wang, Zhan, Qiuqi Ruan, and Gaoyun An. "Face Recognition Using Double Sparse Local Fisher Discriminant Analysis." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/636928.

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Local Fisher discriminant analysis (LFDA) was proposed for dealing with the multimodal problem. It not only combines the idea of locality preserving projections (LPP) for preserving the local structure of the high-dimensional data but also combines the idea of Fisher discriminant analysis (FDA) for obtaining the discriminant power. However, LFDA also suffers from the undersampled problem as well as many dimensionality reduction methods. Meanwhile, the projection matrix is not sparse. In this paper, we propose double sparse local Fisher discriminant analysis (DSLFDA) for face recognition. The p

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Sturtevant, Nathan. "A Sparse Grid Representation for Dynamic Three-Dimensional Worlds." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 7, no. 1 (2011): 73–78. http://dx.doi.org/10.1609/aiide.v7i1.12438.

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Grid representations offer many advantages for path planning. Lookups in grids are fast, due to the uniform memory layout, and it is easy to modify grids. But, grids often have significant memory requirements, they cannot directly represent more complex surfaces, and path planning is slower due to their high granularity representation of the world. The speed of path planning on grids has been addressed using abstract representations, such as has been documented in work on Dragon Age: Origins. The abstract representation used in this game was compact, preventing permanent changes to the grid. I

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Majeed, Abdul, and Sungchang Lee. "A Fast Global Flight Path Planning Algorithm Based on Space Circumscription and Sparse Visibility Graph for Unmanned Aerial Vehicle." Electronics 7, no. 12 (2018): 375. http://dx.doi.org/10.3390/electronics7120375.

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This paper proposes a new flight path planning algorithm that finds collision-free, optimal/near-optimal and flyable paths for unmanned aerial vehicles (UAVs) in three-dimensional (3D) environments with fixed obstacles. The proposed algorithm significantly reduces pathfinding computing time without significantly degrading path lengths by using space circumscription and a sparse visibility graph in the pathfinding process. We devise a novel method by exploiting the information about obstacle geometry to circumscribe the search space in the form of a half cylinder from which a working path for U

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Li, Zixuan, Hao Li, Kenli Li, Fan Wu, Lydia Chen, and Keqin Li. "Locality Sensitive Hash Aggregated Nonlinear Neighborhood Matrix Factorization for Online Sparse Big Data Analysis." ACM/IMS Transactions on Data Science 2, no. 4 (2021): 1–27. http://dx.doi.org/10.1145/3497749.

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Matrix factorization (MF) can extract the low-rank features and integrate the information of the data manifold distribution from high-dimensional data, which can consider the nonlinear neighborhood information. Thus, MF has drawn wide attention for low-rank analysis of sparse big data, e.g., Collaborative Filtering (CF) Recommender Systems, Social Networks, and Quality of Service. However, the following two problems exist: (1) huge computational overhead for the construction of the Graph Similarity Matrix (GSM) and (2) huge memory overhead for the intermediate GSM. Therefore, GSM-based MF, e.g

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Muralinath, Rashmi N., Vishwambhar Pathak, and Prabhat K. Mahanti. "Metastable Substructure Embedding and Robust Classification of Multichannel EEG Data Using Spectral Graph Kernels." Future Internet 17, no. 3 (2025): 102. https://doi.org/10.3390/fi17030102.

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Classification of neurocognitive states from Electroencephalography (EEG) data is complex due to inherent challenges such as noise, non-stationarity, non-linearity, and the high-dimensional and sparse nature of connectivity patterns. Graph-theoretical approaches provide a powerful framework for analysing the latent state dynamics using connectivity measures across spatio-temporal-spectral dimensions. This study applies the graph Koopman embedding kernels (GKKE) method to extract latent neuro-markers of seizures from epileptiform EEG activity. EEG-derived graphs were constructed using correlati

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Yang, Ye, Yongli Hu, and Fei Wu. "Sparse and Low-Rank Subspace Data Clustering with Manifold Regularization Learned by Local Linear Embedding." Applied Sciences 8, no. 11 (2018): 2175. http://dx.doi.org/10.3390/app8112175.

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Data clustering is an important research topic in data mining and signal processing communications. In all the data clustering methods, the subspace spectral clustering methods based on self expression model, e.g., the Sparse Subspace Clustering (SSC) and the Low Rank Representation (LRR) methods, have attracted a lot of attention and shown good performance. The key step of SSC and LRR is to construct a proper affinity or similarity matrix of data for spectral clustering. Recently, Laplacian graph constraint was introduced into the basic SSC and LRR and obtained considerable improvement. Howev

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Liang, Zexiao, Ruyi Gong, Guoliang Tan, Shiyin Ji, and Ruidian Zhan. "A Frequency Domain Kernel Function-Based Manifold Dimensionality Reduction and Its Application for Graph-Based Semi-Supervised Classification." Applied Sciences 14, no. 12 (2024): 5342. http://dx.doi.org/10.3390/app14125342.

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With the increasing demand for high-resolution images, handling high-dimensional image data has become a key aspect of intelligence algorithms. One effective approach is to preserve the high-dimensional manifold structure of the data and find the accurate mappings in a lower-dimensional space. However, various non-sparse, high-energy occlusions in real-world images can lead to erroneous calculations of sample relationships, invalidating the existing distance-based manifold dimensionality reduction techniques. Many types of noise are difficult to capture and filter in the original domain but ca

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Bradley, Patrick, Sina Keller, and Martin Weinmann. "Unsupervised Feature Selection Based on Ultrametricity and Sparse Training Data: A Case Study for the Classification of High-Dimensional Hyperspectral Data." Remote Sensing 10, no. 10 (2018): 1564. http://dx.doi.org/10.3390/rs10101564.

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In this paper, we investigate the potential of unsupervised feature selection techniques for classification tasks, where only sparse training data are available. This is motivated by the fact that unsupervised feature selection techniques combine the advantages of standard dimensionality reduction techniques (which only rely on the given feature vectors and not on the corresponding labels) and supervised feature selection techniques (which retain a subset of the original set of features). Thus, feature selection becomes independent of the given classification task and, consequently, a subset o

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Bambi, Jonas, Yudi Santoso, Hanieh Sadri, et al. "A Methodological Approach to Extracting Patterns of Service Utilization from a Cross-Continuum High Dimensional Healthcare Dataset to Support Care Delivery Optimization for Patients with Complex Problems." BioMedInformatics 4, no. 2 (2024): 946–65. http://dx.doi.org/10.3390/biomedinformatics4020053.

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Background: Optimizing care for patients with complex problems entails the integration of clinically appropriate problem-specific clinical protocols, and the optimization of service-system-encompassing clinical pathways. However, alignment of service system operations with Clinical Practice Guidelines (CPGs) is far more challenging than the time-bounded alignment of procedures with protocols. This is due to the challenge of identifying longitudinal patterns of service utilization in the cross-continuum data to assess adherence to the CPGs. Method: This paper proposes a new methodology for iden

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Zhou, Ruqin, and Wanshou Jiang. "A Ridgeline-Based Terrain Co-registration for Satellite LiDAR Point Clouds in Rough Areas." Remote Sensing 12, no. 13 (2020): 2163. http://dx.doi.org/10.3390/rs12132163.

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It is still a completely new and challenging task to register extensive, enormous and sparse satellite light detection and ranging (LiDAR) point clouds. Aimed at this problem, this study provides a ridgeline-based terrain co-registration method in preparation for satellite LiDAR point clouds in rough areas. This method has several merits: (1) only ridgelines are extracted as neighbor information for feature description and their intersections are extracted as keypoints, which can greatly reduce the number of points for subsequent processing, and extracted keypoints is of high repeatability and

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Grassi, Mario, and Barbara Tarantino. "SEMdag: Fast learning of Directed Acyclic Graphs via node or layer ordering." PLOS ONE 20, no. 1 (2025): e0317283. https://doi.org/10.1371/journal.pone.0317283.

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A Directed Acyclic Graph (DAG) offers an easy approach to define causal structures among gathered nodes: causal linkages are represented by arrows between the variables, leading from cause to effect. Recently, industry and academics have paid close attention to DAG structure learning from observable data, and many techniques have been put out to address the problem. We provide a two-step approach, named SEMdag(), that can be used to quickly learn high-dimensional linear SEMs. It is included in the R package SEMgraph and employs a two-stage order-based search using previous knowledge (Knowledge

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Liang, Jiaxuan, Jun Wang, Guoxian Yu, Shuyin Xia, and Guoyin Wang. "Multi-Granularity Causal Structure Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13727–35. http://dx.doi.org/10.1609/aaai.v38i12.29278.

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Unveiling, modeling, and comprehending the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines. Meanwhile, new knowledge emerges when discovering causal relationships from data. Existing causal learning algorithms predominantly focus on the isolated effects of variables, overlook the intricate interplay of multiple variables and their collective behavioral patterns. Furthermore, the ubiquity of high-dimensional data exacts a substantial temporal cost for causal algorithms. In this paper, we develop a novel method called MgCSL (Mu

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Majeed, Abdul, and Seong Oun Hwang. "Path Planning Method for UAVs Based on Constrained Polygonal Space and an Extremely Sparse Waypoint Graph." Applied Sciences 11, no. 12 (2021): 5340. http://dx.doi.org/10.3390/app11125340.

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Finding an optimal/quasi-optimal path for Unmanned Aerial Vehicles (UAVs) utilizing full map information yields time performance degradation in large and complex three-dimensional (3D) urban environments populated by various obstacles. A major portion of the computing time is usually wasted on modeling and exploration of spaces that have a very low possibility of providing optimal/sub-optimal paths. However, computing time can be significantly reduced by searching for paths solely in the spaces that have the highest priority of providing an optimal/sub-optimal path. Many Path Planning (PP) tec

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Kiesel, Scott, Ethan Burns, and Wheeler Ruml. "Abstraction-Guided Sampling for Motion Planning." Proceedings of the International Symposium on Combinatorial Search 3, no. 1 (2021): 162–63. http://dx.doi.org/10.1609/socs.v3i1.18265.

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Motion planning in continuous space is a fundamentalrobotics problem that has been approached from many per-spectives. Rapidly-exploring Random Trees (RRTs) usesampling to efficiently traverse the continuous and high-dimensional state space. Heuristic graph search methods uselower bounds on solution cost to focus effort on portions ofthe space that are likely to be traversed by low-cost solutions.In this work, we bring these two ideas together in a tech-nique called f -biasing: we use estimates of solution cost,computed as in heuristic search, to guide sparse sampling,as in RRTs. We see this n

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Federico, Anthony, Joseph Kern, Xaralabos Varelas, and Stefano Monti. "Structure Learning for Gene Regulatory Networks." PLOS Computational Biology 19, no. 5 (2023): e1011118. http://dx.doi.org/10.1371/journal.pcbi.1011118.

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Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput “omics” data typically available. To overcome this challenge, often referred to as the “small n, large p problem,” we exploit known organizing principles of biological networks that are sparse, modular, and likely share a large portion of their underlying architecture. We present SHINE—Structure Learning for Hierarchical Networks—a framework for defining data-driven structural constraints and incorporating a shared learning paradigm for efficient

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Wang, Zhen, Yongjie Wang, Xinli Xiong, Qiankun Ren, and Jun Huang. "A Novel Framework for Enhancing Decision-Making in Autonomous Cyber Defense Through Graph Embedding." Entropy 27, no. 6 (2025): 622. https://doi.org/10.3390/e27060622.

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Faced with challenges posed by sophisticated cyber attacks and dynamic characteristics of cyberspace, the autonomous cyber defense (ACD) technology has shown its effectiveness. However, traditional decision-making methods for ACD are unable to effectively characterize the network topology and internode dependencies, which makes it difficult for defenders to identify key nodes and critical attack paths. Therefore, this paper proposes an enhanced decision-making method combining graph embedding with reinforcement learning algorithms. By constructing a game model for cyber confrontations, this pa

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Lu, Pengli, Junxia Yang, and Teng Zhang. "Identifying influential nodes in complex networks based on network embedding and local structure entropy." Journal of Statistical Mechanics: Theory and Experiment 2023, no. 8 (2023): 083402. http://dx.doi.org/10.1088/1742-5468/acdceb.

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Abstract The identification of influential nodes in complex networks remains a crucial research direction, as it paves the way for analyzing and controlling information diffusion. The currently presented network embedding algorithms are capable of representing high-dimensional and sparse networks with low-dimensional and dense vector spaces, which not only keeps the network structure but also has high accuracy. In this work, a novel centrality approach based on network embedding and local structure entropy, called the ELSEC, is proposed for capturing richer information to evaluate the importan

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Wang, Binhao, Jianwei Liu, Bing Kuang, Yuwei Li, and Xianfeng Luo. "Research on 3D laser SLAM algorithm based on graph optimization." Journal of Physics: Conference Series 2880, no. 1 (2024): 012001. http://dx.doi.org/10.1088/1742-6596/2880/1/012001.

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Abstract Aiming at the problems that the positioning accuracy of laser Simultaneous Localization and Mapping (SLAM) is affected by the misclassification of close feature points and incorrect feature matching in the environment with redundant features, a 3D laser SLAM algorithm based on graph optimization was proposed. Firstly, to reduce the feature extraction error problem, the distance threshold is used to filter the close point cloud and improve the accuracy of feature classification, enhanced reliability of optoelectronic measurement data. Secondly, to improve the consistency of feature mat

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Yang, Yazhi, Jiandong Shi, Siwei Zhou, and Shasha Yang. "Geometric Matrix Completion via Graph-Based Truncated Norm Regularization for Learning Resource Recommendation." Mathematics 12, no. 2 (2024): 320. http://dx.doi.org/10.3390/math12020320.

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In the competitive landscape of online learning, developing robust and effective learning resource recommendation systems is paramount, yet the field faces challenges due to high-dimensional, sparse matrices and intricate user–resource interactions. Our study focuses on geometric matrix completion (GMC) and introduces a novel approach, graph-based truncated norm regularization (GBTNR) for problem solving. GBTNR innovatively incorporates truncated Dirichlet norms for both user and item graphs, enhancing the model’s ability to handle complex data structures. This method synergistically combines

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Bambi, Jonas, Hanieh Sadri, Ken Moselle, et al. "Approaches to Extracting Patterns of Service Utilization for Patients with Complex Conditions: Graph Community Detection vs. Natural Language Processing Clustering." BioMedInformatics 4, no. 3 (2024): 1884–900. http://dx.doi.org/10.3390/biomedinformatics4030103.

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Background: As patients interact with a healthcare service system, patterns of service utilization (PSUs) emerge. These PSUs are embedded in the sparse high-dimensional space of longitudinal cross-continuum health service encounter data. Once extracted, PSUs can provide quality assurance/quality improvement (QA/QI) efforts with the information required to optimize service system structures and functions. This may improve outcomes for complex patients with chronic diseases. Method: Working with longitudinal cross-continuum encounter data from a regional health service system, various pattern de

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R., Kalai Selvi, and Malathy G. "Data Structure Innovations for Machine Learning and AI Algorithms." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 2640–43. https://doi.org/10.5281/zenodo.14890846.

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With the increasing complexity and size of data in machine learning (ML) and artificial intelligence (AI) applications, efficient data structures have become critical for enhancing performance, scalability, and memory management. Traditional data structures often fail to meet the specific requirements of modern ML and AI algorithms, particularly in terms of speed, flexibility, and storage efficiency. This paper explores recent innovations in data structures  tailored for ML and AI tasks, including dynamic data structures, compressed storage techniques, and specialized graph- based structu

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Song, Wenjun, Xinqi Liu, and Qiuwen Zhang. "Fast Coding Unit Partitioning Method for Video-Based Point Cloud Compression: Combining Convolutional Neural Networks and Bayesian Optimization." Electronics 14, no. 7 (2025): 1295. https://doi.org/10.3390/electronics14071295.

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As 5G technology and 3D capture techniques have been rapidly developing, there has been a remarkable increase in the demand for effectively compressing dynamic 3D point cloud data. Video-based point cloud compression (V-PCC), which is an innovative method for 3D point cloud compression, makes use of High-Efficiency Video Coding (HEVC) to carry out the compression of 3D point clouds. This is accomplished through the projection of the point clouds onto two-dimensional video frames. However, V-PCC faces significant coding complexity, particularly for dynamic 3D point clouds, which can be up to fo

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Bi, Yifei, Jianing Luo, Jiwei Zhu, Junxiu Liu, and Wei Li. "Decentralized Multi-Robot Navigation Based on Deep Reinforcement Learning and Trajectory Optimization." Biomimetics 10, no. 6 (2025): 366. https://doi.org/10.3390/biomimetics10060366.

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Multi-robot systems are significant in decision-making capabilities and applications, but avoiding collisions during movement remains a critical challenge. Existing decentralized obstacle avoidance strategies, while low in computational cost, often fail to ensure safety effectively. To address this issue, this paper leverages graph neural networks (GNNs) and deep reinforcement learning (DRL) to aggregate high-dimensional features as inputs for reinforcement learning (RL) to generate paths. Additionally, it introduces safety constraints through an artificial potential field (APF) to optimize th

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Norville, Zane, Michelle Hedlund, and Vivek Buch. "1299 Network-Wide Morphological Dynamics Predict Functional Cognitive Performance." Neurosurgery 71, Supplement_1 (2025): 214. https://doi.org/10.1227/neu.0000000000003360_1299.

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INTRODUCTION: Learning-related morphological changes in brain connectivity remain poorly understood. Network analysis techniques provide robust interpretation of high-dimensional neural data during skill acquisition. This insight may reveal novel anatomical and personalized targets for restoring performance in individuals with intellectual disabilities, who today possess sparse therapeutic offerings. METHODS: Task-paired electrode data were collected from 23 subjects undergoing sEEG epilepsy evaluation. Each subject performed several trials of a temporal expectancy task paradigm in which react

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Zhang, Zhihao. "A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology." Scientific Programming 2021 (October 28, 2021): 1–9. http://dx.doi.org/10.1155/2021/9531111.

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Through the current research on e-learning, it is found that the present e-learning system applied to the recommendation activities of learning resources has only two search methods: Top-N and keywords. These search methods cannot effectively recommend learning resources to learners. Therefore, the collaborative filtering recommendation technology is applied, in this paper, to the process of personalized recommendation of learning resources. We obtain user content and functional interest and predict the comprehensive interest of web and big data through an infinite deep neural network. Based o

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Sanz Ilundain, Iñigo, Laura Hernández-Lorenzo, Cristina Rodríguez-Antona, Jesús García-Donas, and José L. Ayala. "Autoencoder techniques for survival analysis on renal cell carcinoma." PLOS One 20, no. 5 (2025): e0321045. https://doi.org/10.1371/journal.pone.0321045.

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Survival is the gold standard in oncology when determining the real impact of therapies in patients outcome. Thus, identifying molecular predictors of survival (like genetic alterations or transcriptomic patterns of gene expression) is one of the most relevant fields in current research. Statistical methods and metrics to analyze time-to-event data are crucial in understanding disease progression and the effectiveness of treatments. However, in the medical field, data is often high-dimensional, complicating the application of such methodologies. In this study, we addressed this challenge by co

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Yue, Yuyu, Jixin Zhang, Mingwu Zhang, and Jia Yang. "An Abnormal Account Identification Method by Topology Feature Analysis for Blockchain-Based Transaction Network." Electronics 13, no. 8 (2024): 1416. http://dx.doi.org/10.3390/electronics13081416.

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Cryptocurrency, as one of the most successful applications of blockchain technology, has played a vital role in promoting the development of the digital economy. However, its anonymity, large scale of cryptographic transactions, and decentralization have also brought new challenges in identifying abnormal accounts and preventing abnormal transaction behaviors, such as money laundering, extortion, and market manipulation. Recently, some researchers have proposed efficient and accurate abnormal transaction detection based on machine learning. However, in reality, abnormal accounts and transactio

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Javidian, Mohammad Ali, Marco Valtorta, and Pooyan Jamshidi. "AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms." Journal of Artificial Intelligence Research 69 (October 7, 2020): 419–70. http://dx.doi.org/10.1613/jair.1.12101.

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This paper deals with chain graphs (CGs) under the Andersson–Madigan–Perlman (AMP) interpretation. We address the problem of finding a minimal separator in an AMP CG, namely, finding a set Z of nodes that separates a given non-adjacent pair of nodes such that no proper subset of Z separates that pair. We analyze several versions of this problem and offer polynomial time algorithms for each. These include finding a minimal separator from a restricted set of nodes, finding a minimal separator for two given disjoint sets, and testing whether a given separator is minimal. To address the problem of

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Chen, Junting, Liyun Zhong, and Caiyun Cai. "Using Exponential Kernel for Semi-Supervised Word Sense Disambiguation." Journal of Computational and Theoretical Nanoscience 13, no. 10 (2016): 6929–34. http://dx.doi.org/10.1166/jctn.2016.5649.

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Word sense disambiguation (WSD) in natural language text is a fundamental semantic understanding task at the lexical level in natural language processing (NLP) applications. Kernel methods such as support vector machine (SVM) have been successfully applied to WSD. This is mainly due to their relatively high classification accuracy as well as their ability to handle high dimensional and sparse data. A significant challenge in WSD is to reduce the need for labeled training data while maintaining an acceptable performance. In this paper, we present a semi-supervised technique using the exponentia

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Zhao, Haitao, Sujay Datta, and Zhong-Hui Duan. "An Integrated Approach of Learning Genetic Networks From Genome-Wide Gene Expression Data Using Gaussian Graphical Model and Monte Carlo Method." Bioinformatics and Biology Insights 17 (January 2023): 117793222311529. http://dx.doi.org/10.1177/11779322231152972.

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Global genetic networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single genes or local networks. The Gaussian graphical model (GGM) is widely applied to learn genetic networks because it defines an undirected graph decoding the conditional dependence between genes. Many algorithms based on the GGM have been proposed for learning genetic network structures. Because the number of gene variables is typically far more than the number of samples collected, and a real genetic network is typically sparse, the graphical lasso im

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