Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
A Dual Cross-Attention Graph Learning Framework For Multimodal MRI-Based Major Depressive Disorder Detection
Major depressive disorder (MDD) is a prevalent mental disorder associated with complex neurobiological changes that cannot be fully captured using a single imaging modality. The use of multimodal magnetic resonance imagi…
Graph LearningA Closer Look at the Application of Causal Inference in Graph Representation Learning
Modeling causal relationships in graph representation learning remains a fundamental challenge. Existing approaches often draw on theories and methods from causal inference to identify causal subgraphs or mitigate confou…
Graph Representation LearningCausal InferenceGraph LearningNeighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learning
Graph neural networks (GNNs) have been widely adopted in engineering applications such as social network analysis, chemical research and computer vision. However, their efficacy is severely compromised by the inherent ho…
Node ClassificationGraph LearningMulti-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks
Accurate phase equilibria are foundational to alloy design because they encode the underlying thermodynamics governing stability, transformations, and processing windows. However, while the CALculation of Phase Diagrams …
Graph LearningToward Generalizable Graph Learning for 3D Engineering AI: Explainable Workflows for CAE Mode Shape Classification and CFD Field Prediction
Automotive engineering development increasingly relies on heterogeneous 3D data, including finite element (FE) models, body-in-white (BiW) representations, CAD geometry, and CFD meshes. At the same time, engineering team…
Graph LearningAgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning
Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool use, and decision-making-to overcome the limits of static, parametric knowledge. Yet existing agentic frameworks treat exte…
Reinforcement LearningNode ClassificationLink PredictionGraph LearningEAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networks
Modern logistics networks generate rich operational data streams at every warehouse node and transportation lane -- from order timestamps and routing records to shipping manifests -- yet predicting delivery delays remain…
Multi-Task LearningAnomaly DetectionGraph LearningLearning Superpixel Ensemble and Hierarchy Graphs for Melanoma Detection
Graph signal processing (GSP) is becoming a major tool in biomedical signal and image analysis. In most GSP techniques, graph structures and edge weights have been typically set via statistical and computational methods.…
Graph structure learningData AugmentationGraph LearningAnalytic Drift Resister for Non-Exemplar Continual Graph Learning
Non-Exemplar Continual Graph Learning (NECGL) seeks to eliminate the privacy risks intrinsic to rehearsal-based paradigms by retaining solely class-level prototype representations rather than raw graph examples for mitig…
class-incremental learningNode ClassificationContinual LearningGraph LearningSTDDN: A Physics-Guided Deep Learning Framework for Crowd Simulation
Accurate crowd simulation is crucial for public safety management, emergency evacuation planning, and intelligent transportation systems. However, existing methods, which typically model crowds as a collection of indepen…
Trajectory PredictionGraph LearningTowards Intelligent Energy Security: A Unified Spatio-Temporal and Graph Learning Framework for Scalable Electricity Theft Detection in Smart Grids
Electricity theft and non-technical losses (NTLs) remain critical challenges in modern smart grids, causing significant economic losses and compromising grid reliability. This study introduces the SmartGuard Energy Intel…
Feature EngineeringEnsemble LearningGraph LearningBiTA: Bidirectional Gated Recurrent Unit-Transformer Aggregator in a Temporal Graph Network Framework for Alert Prediction in Computer Networks
Proactive alert prediction in computer networks is critical for mitigating evolving cyber threats and enabling timely defensive actions. Temporal Graph Neural Networks (TGNs) provide a principled framework for modeling t…
Intrusion DetectionGraph LearningDisentangled Dual-Branch Graph Learning for Conversational Emotion Recognition
Multimodal emotion recognition in conversations aims to infer utterance-level emotions by jointly modeling textual, acoustic, and visual cues within context. Despite recent progress, key challenges remain, including redu…
Multimodal Emotion RecognitionGraph Neural NetworkGraph LearningCausality-inspired Federated Learning for Dynamic Spatio-Temporal Graphs
Federated Graph Learning (FGL) has emerged as a powerful paradigm for decentralized training of graph neural networks while preserving data privacy. However, existing FGL methods are predominantly designed for static gra…
Contrastive LearningFederated LearningGraph LearningORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment
Although Graph Neural Networks (GNNs) have shown promise for smart contract vulnerability detection, they still face significant limitations. Homogeneous graph models fail to capture the interplay between control flow an…
Vulnerability DetectionGraph LearningCross-attentive Cohesive Subgraph Embedding to Mitigate Oversquashing in GNNs
Graph neural networks (GNNs) have achieved strong performance across various real-world domains. Nevertheless, they suffer from oversquashing, where long-range information is distorted as it is compressed through limited…
Graph LearningTMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution
Multimodal-attributed graphs (MAGs) are a fundamental data structure for multimodal graph learning (MGL), enabling both graph-centric and modality-centric tasks. However, our empirical analysis reveals inherent topology …
Metric LearningGraph LearningLineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networks
Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading to suboptimal accuracy and a lack of scalability. Graph neur…
Adversarial RobustnessGraph Neural NetworkGraph LearningCSTS: A Canonical Security Telemetry Substrate for AI-Native Cyber Detection
Cybersecurity data remains fragmented across vendors, formats, schemas, and deployment environments, forcing AI and analytics programs to spend disproportionate effort on ingestion, normalization, and brittle source-spec…
Anomaly DetectionGraph LearningRiemannian Geometry Speaks Louder Than Words: From Graph Foundation Model to Next-Generation Graph Intelligence
Graphs provide a natural description of the complex relationships among objects, and play a pivotal role in communications, transportation, social computing, the life sciences, etc. Currently, there is strong agreement t…
Graph Learning