Papers Graph structure learning
“Graph structure learning” 태그가 달린 논문 161편 · 필터 해제
Uncertainty-Aware Graph Structure Learning
Graph Neural Networks (GNNs) have become a prominent approach for learning from graph-structured data. However, their effectiveness can be significantly compromised when the graph structure is suboptimal. To address this…
Graph structure learningGraph Structure Learning for Tumor Microenvironment with Cell Type Annotation from non-spatial scRNA-seq data
The exploration of cellular heterogeneity within the tumor microenvironment (TME) via single-cell RNA sequencing (scRNA-seq) is essential for understanding cancer progression and response to therapy. Current scRNA-seq ap…
Graph Neural NetworkGraph structure learningType predictionExtracting Inter-Protein Interactions Via Multitasking Graph Structure Learning
Identifying protein-protein interactions (PPI) is crucial for gaining in-depth insights into numerous biological processes within cells and holds significant guiding value in areas such as drug development and disease tr…
Graph AttentionGraph structure learningPredictionA Comprehensive Survey on Spectral Clustering with Graph Structure Learning
Spectral clustering is a powerful technique for clustering high-dimensional data, utilizing graph-based representations to detect complex, non-linear structures and non-convex clusters. The construction of a similarity g…
Clusteringgraph constructionGraph structure learningSurveyKANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes
Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improving accuracy. However, they overlook the n…
Graph AttentionGraph structure learningRepresentation LearningGraph Structure Learning for Spatial-Temporal Imputation: Adapting to Node and Feature Scales
Spatial-temporal data collected across different geographic locations often suffer from missing values, posing challenges to data analysis. Existing methods primarily leverage fixed spatial graphs to impute missing value…
Graph structure learningImputationMissing ValuesRepresentation LearningDG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models
Dynamic graphs exhibit intertwined spatio-temporal evolutionary patterns, widely existing in the real world. Nevertheless, the structure incompleteness, noise, and redundancy result in poor robustness for Dynamic Graph N…
Graph structure learningInformativenessMambaState Space ModelsFine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning
AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there is a growing consensus that intelligent…
Contrastive LearningGraph Representation LearningGraph structure learningKnowledge Graphs+2Graph Structure Learning with Bi-level Optimization
Currently, most Graph Structure Learning (GSL) methods, as a means of learning graph structure, improve the robustness of GNN merely from a local view by considering the local information related to each edge and indiscr…
Graph structure learningRethinking Structure Learning For Graph Neural Networks
To improve the performance of Graph Neural Networks (GNNs), Graph Structure Learning (GSL) has been extensively applied to reconstruct or refine original graph structures, effectively addressing issues like heterophily, …
Graph structure learningGaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck
Graph neural networks (GNNs) are prominent for their effectiveness in processing graph data for semi-supervised node classification tasks. Most works of GNNs assume that the observed structure accurately represents the u…
Graph structure learningNode ClassificationBi-Level Graph Structure Learning for Next POI Recommendation
Next point-of-interest (POI) recommendation aims to predict a user's next destination based on sequential check-in history and a set of POI candidates. Graph neural networks (GNNs) have demonstrated a remarkable capabili…
Graph structure learningHypergraph-based multi-scale spatio-temporal graph convolution network for Time-Series anomaly detection
Multivariate time series anomaly detection technology plays an important role in many fields including aerospace, water treatment, cloud service providers, etc. Excellent anomaly detection models can greatly improve work…
Anomaly DetectionGraph structure learningTime SeriesTime Series Anomaly DetectionBridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning
Graph representation learning, involving both node features and graph structures, is crucial for real-world applications but often encounters pervasive noise. State-of-the-art methods typically address noise by focusing …
Graph Representation LearningGraph structure learningRepresentation LearningSmall Language ModelSpectrum Prediction via Graph Structure Learning
With the rapid development of machine learning technologies, data-driven spectrum prediction enables intelligent dynamic spectrum access to alleviate the bottleneck of spectrum resource scarcity and congestion. However, …
Graph structure learningPredictionTime Series PredictionBeyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning
Unsupervised Multiplex Graph Learning (UMGL) aims to learn node representations on various edge types without manual labeling. However, existing research overlooks a key factor: the reliability of the graph structure. Re…
Contrastive LearningGraph LearningGraph structure learningBoolean Product Graph Neural Networks
Graph Neural Networks (GNNs) have recently achieved significant success, with a key operation involving the aggregation of information from neighboring nodes. Substantial researchers have focused on defining neighbors fo…
Graph structure learningOnline Proximal ADMM for Graph Learning from Streaming Smooth Signals
Graph signal processing deals with algorithms and signal representations that leverage graph structures for multivariate data analysis. Often said graph topology is not readily available and may be time-varying, hence (d…
Graph LearningGraph structure learningParkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations
Parkinson's disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual's quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroenceph…
Contrastive LearningEEGGraph AttentionGraph Neural Network+1GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image Classification
Efficient extraction of spectral sequences and geospatial information has always been a hot topic in hyperspectral image classification. In terms of spectral sequence feature capture, RNN and Transformer have become main…
Computational EfficiencyGraph structure learningHyperspectral Image Classificationimage-classification+2