Papers Edge Classification
“Edge Classification” 태그가 달린 논문 56편 · 필터 해제
Scaling Graph Neural Networks for Particle Track Reconstruction
Particle track reconstruction is an important problem in high-energy physics (HEP), necessary to study properties of subatomic particles. Traditional track reconstruction algorithms scale poorly with the number of partic…
Edge ClassificationGPULLM-driven Knowledge Distillation for Dynamic Text-Attributed Graphs
Dynamic Text-Attributed Graphs (DyTAGs) have numerous real-world applications, e.g. social, collaboration, citation, communication, and review networks. In these networks, nodes and edges often contain text descriptions,…
Edge ClassificationKnowledge DistillationLink PredictionDisentangling Popularity and Quality: An Edge Classification Approach for Fair Recommendation
Graph neural networks (GNNs) have proven to be an effective tool for enhancing the performance of recommender systems. However, these systems often suffer from popularity bias, leading to an unfair advantage for frequent…
Edge ClassificationFairnessRecommendation SystemsLocal and Global Graph Modeling with Edge-weighted Graph Attention Network for Handwritten Mathematical Expression Recognition
In this paper, we present a novel approach to Handwritten Mathematical Expression Recognition (HMER) by leveraging graph-based modeling techniques. We introduce an End-to-end model with an Edge-weighted Graph Attention M…
ClassificationEdge ClassificationGraph AttentionRelation ClassificationMetric-Semantic Factor Graph Generation based on Graph Neural Networks
Understanding the relationships between geometric structures and semantic concepts is crucial for building accurate models of complex environments. In indoors, certain spatial constraints, such as the relative positionin…
AttributeEdge ClassificationGraph GenerationA GREAT Architecture for Edge-Based Graph Problems Like TSP
In the last years, many neural network-based approaches have been proposed to tackle combinatorial optimization problems such as routing problems. Many of these approaches are based on graph neural networks (GNNs) or rel…
Combinatorial OptimizationEdge ClassificationTraveling Salesman ProblemRTAT: A Robust Two-stage Association Tracker for Multi-Object Tracking
Data association is an essential part in the tracking-by-detection based Multi-Object Tracking (MOT). Most trackers focus on how to design a better data association strategy to improve the tracking performance. The rule-…
Edge ClassificationMulti-Object TrackingObject TrackingEggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction
Track reconstruction is a crucial task in particle experiments and is traditionally very computationally expensive due to its combinatorial nature. Recently, graph neural networks (GNNs) have emerged as a promising appro…
Edge ClassificationGraph AttentionEffective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs
Graph representation learning (GRL) is to encode graph elements into informative vector representations, which can be used in downstream tasks for analyzing graph-structured data and has seen extensive applications in va…
AttributeEdge ClassificationGraph Representation LearningRepresentation LearningEdge Classification on Graphs: New Directions in Topological Imbalance
Recent years have witnessed the remarkable success of applying Graph machine learning (GML) to node/graph classification and link prediction. However, edge classification task that enjoys numerous real-world applications…
ClassificationEdge ClassificationGraph ClassificationLink Prediction+1DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs
Dynamic text-attributed graphs (DyTAGs) are prevalent in various real-world scenarios, where each node and edge are associated with text descriptions, and both the graph structure and text descriptions evolve over time. …
Dynamic graph embeddingEdge ClassificationGraph LearningLink Prediction+2Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
We introduce a new framework for molecular graph generation with 3D molecular generative models. Our Synthetic Coordinate Embedding (SyCo) framework maps molecular graphs to Euclidean point clouds via synthetic conformer…
Edge ClassificationGraph GenerationGraph Neural NetworkMolecular Graph Generation+1Vision Mamba: Cutting-Edge Classification of Alzheimer's Disease with 3D MRI Scans
Classifying 3D MRI images for early detection of Alzheimer's disease is a critical task in medical imaging. Traditional approaches using Convolutional Neural Networks (CNNs) and Transformers face significant challenges i…
Computational EfficiencyEdge ClassificationMambaState Space ModelsMulti-Modal UAV Detection, Classification and Tracking Algorithm -- Technical Report for CVPR 2024 UG2 Challenge
This technical report presents the 1st winning model for UG2+, a task in CVPR 2024 UAV Tracking and Pose-Estimation Challenge. This challenge faces difficulties in drone detection, UAV-type classification and 2D/3D traje…
ClassificationEdge ClassificationPose EstimationReasoning Algorithmically in Graph Neural Networks
The development of artificial intelligence systems with advanced reasoning capabilities represents a persistent and long-standing research question. Traditionally, the primary strategy to address this challenge involved …
Combinatorial OptimizationEdge ClassificationLogical ReasoningHigh Pileup Particle Tracking with Object Condensation
Recent work has demonstrated that graph neural networks (GNNs) can match the performance of traditional algorithms for charged particle tracking while improving scalability to meet the computing challenges posed by the H…
Edge ClassificationObjectMulti-omics Sampling-based Graph Transformer for Synthetic Lethality Prediction
Synthetic lethality (SL) prediction is used to identify if the co-mutation of two genes results in cell death. The prevalent strategy is to abstract SL prediction as an edge classification task on gene nodes within SL da…
Edge ClassificationPrediction3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking
Tracking 3D objects accurately and consistently is crucial for autonomous vehicles, enabling more reliable downstream tasks such as trajectory prediction and motion planning. Based on the substantial progress in object d…
3D Multi-Object TrackingAutonomous VehiclesEdge ClassificationMotion Planning+6Retrieval Augmented Generation using Engineering Design Knowledge
Aiming to support Retrieval Augmented Generation (RAG) in the design process, we present a method to identify explicit, engineering design facts - {head entity :: relationship :: tail entity} from patented artefact descr…
Common Sense ReasoningEdge ClassificationKnowledge GraphsRAG+5Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs
In-time particle trajectory reconstruction in the Large Hadron Collider is challenging due to the high collision rate and numerous particle hits. Using GNN (Graph Neural Network) on FPGA has enabled superior accuracy wit…
CPUEdge ClassificationGPUGraph Neural Network