Papers Graph Neural Network
“Graph Neural Network” 태그가 달린 논문 4,412편 · 필터 해제
Effective Resistance and Graph Neural Network Reliability in Tissue-Specific Interactomes
Protein function annotation needs to know which predictions to distrust, not only what a model predicts. We ask whether tissue-specific interaction structure carries that information. Our candidate signal is effective re…
Graph Neural NetworkUnified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimation
Power Flow (PF), Optimal Power Flow (OPF), and State Estimation (SE) are fundamental problems in power system analysis, but solving them is computationally expensive. Graph Neural Networks (GNNs) have been proposed as fa…
Graph Neural NetworkFidelity-Aware Scheduling of Quantum Circuits on Multi-QPU Systems
High Performance Computing-Quantum Computing (HPCQC) platforms expose multiple Quantum Processing Units (QPUs) that may differ in size, topology, native gates, and noise characteristics. For current noisy devices, errors…
Graph Neural NetworkProMeta: Few-shot PROTAC-targeted degradation prediction across E3 ligases
Proteolysis-targeting chimeras (PROTACs) have emerged as a transformative therapeutic strategy that selectively degrades historically ''undruggable'' targets via the ubiquitin-proteasome system. Despite growing efforts t…
Graph Neural NetworkActivity PredictionMorphological Decoupling-Based Skeletal Classification for Clinical Assessment of Malocclusion
Malocclusion skeletal grading is a fundamental task in orthodontics, critical for diagnosis and treatment planning. Traditionally, cone-beam computed tomography (CBCT) is used for visual measurement, and the reconstructe…
Graph Neural NetworkPccDiffuser: Multi-solution Motion Planning for Continuum Robots
We present the PccDiffuser, a conditional diffusion framework for continuum robots that learns a multimodal distribution over complete configuration-space paths and samples multiple candidate solutions in parallel, which…
Computational EfficiencyGraph Neural NetworkMotion PlanningA Unified Physics-Aware Quantum Machine Learning Framework across Power GaN HEMTs and Logic Nanowire FETs: Predicting Unseen Process Splits and Held-Out Geometry Combinations with Lower Error and Tighter Split-to-Split Variability
We present a unified reinforcement-learning (RL) framework that discovers compact parametrized quantum circuits (PQCs) for data-scarce device modeling. A graph neural network (GNN) policy optimized by proximal policy opt…
Quantum Machine LearningGraph Neural NetworkDynamic Heterogeneous Graph Representation Learning: A Survey
Graph representation learning (GRL) serves as a canonical paradigm for modeling complex networks. However, real-world AI systems inherently manifest as evolving heterogeneous entities with complex interactions, posing si…
Graph Representation LearningGraph Neural NetworkPredicting Spatiotemporal Mobile Sensing-Based PM2.5 Concentrations Using Low-Rank Adapted Spatially Attentive Graph Neural Network
Urban air quality can vary significantly along transit corridors, necessitating high-resolution monitoring. This work introduces a novel mobile-sensing dataset from Surat, Gujarat, India, comprising PM$*{2.5}$ concentrat…
Graph Neural NetworkGNN-Guided Graph Coarsening and Adaptive QUBO Penalties for the Capacitated Vehicle Routing Problem with Time Windows on a Quantum Annealer
Graph coarsening reduces the large Quadratic Unconstrained Binary Optimization (QUBO) formulations arising when vehicle-routing problems are solved by quantum annealing. Nearby customers with compatible time windows are …
Graph Neural NetworkMolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions
Optimizing absorption, distribution, metabolism, and excretion (ADME) is an important part of small molecule drug discovery. Many machine learning models have been built to predict ADME properties to facilitate this opti…
Graph Neural NetworkDrug DiscoveryBeamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI
Beamforming methods in millimeter-wave (mmWave) cell-free massive multiple-input multiple-output (CFmMIMO) systems require accurate channel state information (CSI), whose acquisition entails significant training overhead…
Graph Neural NetworkGraph4BiLO: Graph Neural Network Approximation for Bilevel Mixed-Integer Linear Optimization
Bilevel mixed-integer linear optimization problems model hierarchical decision processes in which a leader anticipates the optimal response of a follower. Although expressive, these problems are computationally challengi…
Graph Neural NetworkBeyond Flat Netlist: Hierarchical Graph Representation Learning for Scalable Analysis of Sequential Circuits
Circuit Representation Learning (CRL) offers a powerful paradigm to guide and optimize core Electronic Design Automation (EDA) tasks, but its practical adoption is hindered by the immense scale of industrial netlists and…
Graph Representation LearningGraph Neural NetworkTemporal Memory-Aware Online Test-Time Adaptation on Dynamic Graphs
Test-time adaptation (TTA) on graphs aims to adapt a graph neural network (GNN) that is well-trained on the training graph to the test graph, which involves potential distribution shifts that may harm model generalizatio…
Test-time AdaptationGraph Neural NetworkImitation Learning for Connection-Tableau Construction
An automated theorem prover builds a proof step by step, choosing at each point what to add and what to remove. We cast this construction as a policy acting in a transition system induced by a formal calculus, which fixe…
Graph Neural NetworkMetaSieve: Faster Relational Deep Learning through SQL-Based Metapath Selection
Relational Deep Learning (RDL) is an effective approach to machine learning over multi-table relational databases. In RDL, a database is modeled as a graph in which each row is a node and each foreign-key relation is an …
Graph Neural NetworkHierarchy-Aware Semantic Losses for Knowledge Graph Link Prediction
Knowledge graphs are often accompanied by ontological class hierarchies that encode valuable semantic information, yet many link prediction methods either ignore such hierarchies or incorporate them indirectly through ad…
Representation LearningGraph Neural NetworkKnowledge GraphsLink PredictionBeyond Observed Auxiliary Relations: Environment-Conditioned Modeling for Multi-Behavior Recommendation
Multi-behavior recommendation (MBR) leverages auxiliary behavioral signals, such as clicks and add-to-cart, to enhance target behavior prediction like purchases. While recent graph neural network-based approaches have ac…
Graph Neural NetworkGeneralizing Soft Tissue Deformation and Force Prediction Across Material Stiffness and Geometry
Accurate soft tissue simulation is essential for surgical training, pre-operative planning, and haptic feedback systems. While learning-based surrogate models trained on data using the finite element method (FEM) offer a…
Graph Neural Network