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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/thudm/graphalign

GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment

https://github.com/tiago-roxo/asdnb

End-to-End Active Speaker Detection

https://github.com/tiago-roxo/bias

End-to-End Active Speaker Detection

https://github.com/tmdt-buw/schlably

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

https://github.com/tmlr-group/one-shot-subgraph

Knowledge Graph Reasoning with Relational Digraph

https://github.com/toinesayan/node-classification-and-label-dependencies

Pitfalls of Graph Neural Network Evaluation

https://github.com/tomsilver/ploi

Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks

https://github.com/tomyvazquez/doraa-uy

Unsupervised Optimal Power Flow Using Graph Neural Networks

https://github.com/torchspatiotemporal/tsl

Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks

https://github.com/tsinghua-fib-lab/SIGIR21-SURGE

Sequential Recommendation with Graph Neural Networks

https://github.com/tyroneli/cua_o3d

PerLA: Perceptive 3D Language Assistant

https://github.com/tzvikif/SuperGlue

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/venomouscyanide/S3GRL_OGB

Link Prediction Based on Graph Neural Networks

https://github.com/venomouscyanide/s3grl

Link Prediction Based on Graph Neural Networks

https://github.com/venomouscyanide/s3grl_ogb

Link Prediction Based on Graph Neural Networks

https://github.com/vermaMachineLearning/Universal-Graph-Embedding-Neural-Network

Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning

https://github.com/wangf622/GPLight

A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN Prediction

https://github.com/wanjiaZhao1203/TREAT

TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems

https://github.com/wengong-jin/refinegnn

Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design

https://github.com/wenhuchen/gnn-tabfact

NumNet: Machine Reading Comprehension with Numerical Reasoning

https://github.com/wenqifan03/wenqifan03.github.io

Graph Neural Networks for Social Recommendation

https://github.com/westny/dronalize

Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction

https://github.com/willyfh/graph-transformer

Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification

https://github.com/woodcutter1998/ogb-grail-mod

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/wpower12/COVID-19-USSTN

Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks

https://github.com/wumming/graph-pde

Multipole Graph Neural Operator for Parametric Partial Differential Equations

https://github.com/wuziyi616/multi_part_assembly

Generative 3D Part Assembly via Dynamic Graph Learning

https://github.com/wyd1502/DGNN

Streaming Graph Neural Networks

https://github.com/xinyangj/YOLaT-VectorGraphicsRecognition-1

Recognizing Vector Graphics without Rasterization

https://github.com/xinyangz/Inf-VAE

Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction

https://github.com/xjtuwxliang/R-GSN/tree/main

R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph

https://github.com/xk-huang/yet-another-vectornet

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation

https://github.com/xlearning-scu/2023-iccv-common

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/xuzijian629/combopt-zero

Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero

https://github.com/xxxnhb/few-shot-gnn

Few-Shot Learning with Graph Neural Networks

https://github.com/xxxnhb/fewshot-egnn

Edge-labeling Graph Neural Network for Few-shot Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.