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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/migalkin/NodePiece
https://github.com/migalkin/NodePiece/tree/main/ogb
https://github.com/mijungi/vips_code
https://github.com/mims-harvard/raindrop
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
https://github.com/mindspore-ai/contrib/tree/master/intern/RGNN
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
https://github.com/minehly/awesome-paper-for-graph-learning-theory
PAC-Bayesian Adversarially Robust Generalization Bounds for Graph Neural Network
https://github.com/ml-jku/vnegnn
EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
https://github.com/ml4bio/rna-fm
https://github.com/mtiezzi/lpgnn
A Lagrangian Approach to Information Propagation in Graph Neural Networks
https://github.com/muhanzhang/NestedGNN
https://github.com/mukira/PySyft
https://github.com/mwcvitkovic/open-vocabulary-learning-on-source-code-with-a-graph-structured-cache--code-preprocessor
Open Vocabulary Learning on Source Code with a Graph-Structured Cache
https://github.com/nealgravindra/self-supervsed_edge_feats
Self-supervised edge features for improved Graph Neural Network training
https://github.com/negotiatorvivian/PDP-SP
PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers
https://github.com/negotiatorvivian/SAT-Solver
PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers
https://github.com/neural-operator/graph-pde
Multipole Graph Neural Operator for Parametric Partial Differential Equations
https://github.com/neuraloperator/graph-pde
Multipole Graph Neural Operator for Parametric Partial Differential Equations
https://github.com/nhatminh/Dem-AI
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
https://github.com/nhatminh/FEDL
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
https://github.com/nianticlabs/relpose-gnn
Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision
https://github.com/nmvdlei/PPMI_MAGNN
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
https://github.com/nodarsensor/medium-keypoint-autocal
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/nslab-cuk/halal-or-not
https://github.com/ognjenkundacina/graph-neural-network-state-estimation
State Estimation in Electric Power Systems Leveraging Graph Neural Networks
https://github.com/omri1348/LRGA
https://github.com/omri1348/LRGA/tree/master/ogb/examples/linkproppred
https://github.com/omron-sinicx/weavenet
https://github.com/ondrejba/action_attention_iclr_22
https://github.com/open-catalyst-project/adsorbml
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
https://github.com/opendrivelab/hdgt
https://github.com/panwangwin/SpatialTemporalNetTrainer-pytorch
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
https://github.com/peizeli96/Object-attribute-matters-in-visual-question-answering
https://github.com/peizhaoli05/EDA_GNN
Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking
https://github.com/pnnl/expert2
https://github.com/pollyjuice74/5g-decoder
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.