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Graph Neural Network
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/chrsmrrs/speqnets
공식 코드SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks
https://github.com/chrsmrrs/tudataset
공식 코드TUDataset: A collection of benchmark datasets for learning with graphs
https://github.com/chunbaobao/genet
공식 코드GeNet: A Graph Neural Network-based Anti-noise Task-Oriented Semantic Communication Paradigm
https://github.com/chymaera96/neuralsampleid
공식 코드Refining music sample identification with a self-supervised graph neural network
https://github.com/ciam-group/nco_code
공식 코드https://github.com/cis-group/molnet
공식 코드MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties
https://github.com/cjpcool/PR-GAT
공식 코드https://github.com/cjpcool/customgnn
공식 코드https://github.com/cjx96/c2dsr
공식 코드https://github.com/clatfd/GNN-ART-LABEL
공식 코드Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement
https://github.com/clear-nus/TactileSGNet
공식 코드TactileSGNet: A Spiking Graph Neural Network for Event-based Tactile Object Recognition
https://github.com/cmu-safari/pygim
공식 코드PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures
https://github.com/coarse-graining/cgnet
공식 코드Coarse Graining Molecular Dynamics with Graph Neural Networks
https://github.com/codexhammer/CUL
공식 코드https://github.com/codexxxl/GraphVQA
공식 코드GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering
https://github.com/cognitive-robots/sem-gat
공식 코드SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention
https://github.com/coleygroup/polymer-chemprop-data
공식 코드A graph representation of molecular ensembles for polymer property prediction
https://github.com/colorfulfuture/gc-vrnn
공식 코드Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction
https://github.com/compasszzn/degnn
공식 코드Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
https://github.com/corail-research/SeaPearl.jl
공식 코드Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver
https://github.com/corail-research/learning-generic-csp
공식 코드Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches
https://github.com/core-robotics-lab/opponent-modeling
공식 코드Learning Models of Adversarial Agent Behavior under Partial Observability
https://github.com/cripac-dig/graphctr
공식 코드GraphFM: Graph Factorization Machines for Feature Interaction Modeling
https://github.com/critical88/boxgnn
공식 코드When Box Meets Graph Neural Network in Tag-aware Recommendation
https://github.com/cs-bio/torgnn
공식 코드https://github.com/cscvlab/SN-Graph
공식 코드SN-Graph: a Minimalist 3D Object Representation for Classification
https://github.com/cshjin/swmp_ml
공식 코드Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
https://github.com/csiro-robotics/p-gat
공식 코드Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition
https://github.com/cszhangzhen/HGP-SL
공식 코드https://github.com/cuent/ogb-kdd21
공식 코드On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction
https://github.com/cure-lab/DeepGate
공식 코드https://github.com/cure-lab/deeptpi
공식 코드DeepTPI: Test Point Insertion with Deep Reinforcement Learning
https://github.com/cvg/gluestick
공식 코드GlueStick: Robust Image Matching by Sticking Points and Lines Together
https://github.com/cy07gn/teltomob
공식 코드https://github.com/cynricfu/mecch
공식 코드MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.