Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,012편 · 필터 해제
ParasGB: A Graph Benchmark Suite for Parasitic Estimation on AMS Circuits
As chip manufacturing processes advance to deep submicron nodes, parasitic interconnect effects increasingly dominate the performance of analog and mixed-signal (AMS) circuits and often lead to costly layout iterations. …
Parameter PredictionGraph LearningGraph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling
Molecular property prediction from structure often uses a single representative conformation, even though many molecules exist as conformational ensembles in solution. We introduce EnsembleEGNN, a molecular ensemble foun…
Molecular Property PredictionGraph Neural NetworkGraph LearningRegularized Optimization on Grassmann Manifold: Theory, Algorithm and Applications
Spectral methods are among the most widely used techniques for community detection, clustering, and graph learning. Their performance, however, critically depends on the accurate estimation of the underlying spectral sub…
Community DetectionGraph LearningKnowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes
Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process stages. Effective monitoring and timely …
Time Series Anomaly DetectionGraph LearningToward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework
Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-commerce, and biomedical networks, offerin…
Federated LearningFew-Shot LearningGraph LearningDAPGNet: Dynamic Adaptive Physics-Guided Graph Diffusion Network for Hyperspectral Image Classification
Hyperspectral image (HSI) classification requires reliable pixel-relation modeling under spectral variability, mixed pixels, and heterogeneous boundaries. Existing graph-based HSI classifiers usually construct graph topo…
Hyperspectral Image ClassificationGraph LearningEdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy
Graph Neural Networks (GNNs) have shown considerable success in learning from graph-structured data, but their use in privacy-sensitive areas remains difficult because graph structure can leak sensitive link information.…
Graph ClassificationNode ClassificationGraph LearningGraph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion
Denoising graphs is a fundamental problem in graph learning and the core operation of graph diffusion models. Attention-based architectures like graph transformers have recently shown promise in denoising graphs. However…
Graph LearningTowards Personalized Differentially Private Learning for Decentralized Local Graphs
Graph-structured data is increasingly generated and stored in decentralized environments, such as social platforms, mobile applications, and edge networks, where users maintain control over their local graph data. Howeve…
Graph LearningPhysics-Informed Graph Learning with Uncertainty Awareness for Open-Set Domain Generalization in Fault Diagnosis
Intelligent industrial maintenance critically relies on reliable fault diagnosis of rotating machinery. However, it faces formidable challenges from unknown fault types and domain shifts induced by varying operating cond…
Domain GeneralizationFault DiagnosisGraph LearningMKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction
Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development. A difficult setting arises when candidate interactions include proteins that hav…
Graph Representation LearningKnowledge GraphsGraph LearningRobust and Explainable 3D Mode Shape Recognition Using Region-Aware Graph Neural Networks
Mode shape recognition is a fundamental task in automotive NVH development, yet it remains dependent on manual visual inspection by experienced engineers. Existing approaches based on engineering heuristics, Modal Assura…
Graph LearningSAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport
Self-supervised Continual Graph Learning (CGL) aims to successively learn from a graph sequence with different tasks without label supervision - a paradigm that has attracted widespread attention. Most existing self-supe…
Graph Representation LearningKnowledge DistillationContinual LearningGraph LearningTAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning
Text-attributed graphs (TAGs), where each node carries a natural language description, require models to jointly reason over text and graph topology. Existing approaches often handle the two modalities separately: graph …
Node ClassificationLink PredictionGraph LearningPromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph Learning
Text-Attributed Graphs (TAGs) combine textual semantics with graph structure and are central to many graph learning tasks. However, existing fusion methods often treat text and structure as separate inputs in a shallow, …
Contrastive LearningGraph LearningScalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy
Graphs provide a natural language for relational data in chemistry, biology and optimisation. Graph neural networks (GNNs) have driven much of the recent progress in learning from such data through message passing, a sin…
Molecular Property PredictionGraph Neural NetworkGraph LearningA Generalization Theory for JEPA-Based World Models
Joint Embedding Predictive Architectures (JEPAs) have recently emerged as a promising paradigm for world modeling by learning predictive dynamics in a latent space rather than generating future observations at the input …
Graph LearningGRAFT: Biological Graph and Hypergraph Benchmarks for Linked Gene Expression and Phenotypic Trait Prediction in Arabidopsis thaliana
Understanding which genes control which traits in an organism remains one of the central challenges in biology. Despite significant advances in data collection technology, our ability to map genes to traits is still limi…
Graph RegressionGraph LearningWhat Does the Brain See? Multiview Neural Representations to Demystify the Brain-Visual Alignment
Zero-shot visual decoding from electroencephalography (EEG) aims to infer visual semantics from non-invasive neural recordings, but remains challenging due to the low signal-to-noise ratio, non-stationarity, and limited …
Representation LearningContrastive LearningGraph LearningHierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets
Commodity futures can be represented hierarchically, with underlying assets at the upper level and individual futures contracts at the lower level. Entities at each level can be connected by edges reflecting inherent cor…
Graph Learning