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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/qiaoyran/login

공식 코드

LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework

https://github.com/qiaoyu-tan/E2GNN

공식 코드

E2GNN: Efficient Graph Neural Network Ensembles for Semi-Supervised Classification

https://github.com/qiongwu86/optimizing-aoi-in-vec-with-federated-graph-neural-network-multi-agent-reinforcement-learning

공식 코드

Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning

https://github.com/qitianwu/FATE

공식 코드

Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach

https://github.com/qiushuo0913/DT-powered-AMS

공식 코드

Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning

https://github.com/qiyu3816/gdsg

공식 코드

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks

https://github.com/qslim/soft-mask-gnn

공식 코드

Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks

https://github.com/qwq2000/thewebconf24-ltgnn-pytorch

공식 코드

Linear-Time Graph Neural Networks for Scalable Recommendations

https://github.com/radoslav11/sp-mpnn

공식 코드

Shortest Path Networks for Graph Property Prediction

https://github.com/raeidsaqur/clevr-parser

공식 코드

CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes

https://github.com/rama726/capslorentznet

공식 코드

Integrating Physics Inspired Features with Graph Convolution

https://github.com/ranqiu92/NumNet

공식 코드

NumNet: Machine Reading Comprehension with Numerical Reasoning

https://github.com/raphaelsulzer/dgnn

공식 코드

Scalable Surface Reconstruction with Delaunay-Graph Neural Networks

https://github.com/raynbowy23/regt-gcn

공식 코드

Truck Parking Usage Prediction with Decomposed Graph Neural Networks

https://github.com/rccohn/deepspparks

공식 코드

Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution

https://github.com/reail-iitd/tango

공식 코드

TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators

https://github.com/recklessronan/pgnn-ek

공식 코드

A Neural Network Architecture for Program Understanding Inspired by Human Behaviors

https://github.com/renj-xu/negsc

공식 코드

Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network

https://github.com/renli1024/se-gnn

공식 코드

How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View

https://github.com/reysam93/Graph_Deep_Decoder

공식 코드

Untrained Graph Neural Networks for Denoising

https://github.com/reysam93/dag_conv_nn

공식 코드

Convolutional Learning on Directed Acyclic Graphs

https://github.com/riemanngraph/motifrgc

공식 코드

Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning

https://github.com/rina-ding/gat-mamba

공식 코드

Combining Graph Neural Network and Mamba to Capture Local and Global Tissue Spatial Relationships in Whole Slide Images

https://github.com/ringbdstack/ace-hgnn

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ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

https://github.com/ringbdstack/rplm_sed

공식 코드

Relational Prompt-based Pre-trained Language Models for Social Event Detection

https://github.com/risilab/Autobahn

공식 코드

Autobahn: Automorphism-based Graph Neural Nets

https://github.com/ritatsousa/expressionKGplus

공식 코드

Multi-dataset and Transfer Learning Using Gene Expression Knowledge Graphs

https://github.com/rmanluo/np-fkgc

공식 코드

Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion

https://github.com/robbertreijnen/gs-modac

공식 코드

Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization

https://github.com/rochesterxugroup/DSGPM

공식 코드

Graph Neural Network Based Coarse-Grained Mapping Prediction

https://github.com/rochesterxugroup/HAM_dataset

공식 코드

Graph Neural Network Based Coarse-Grained Mapping Prediction

https://github.com/rollingstonezz/sdm24_manifold_spatial_networks

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Non-Euclidean Spatial Graph Neural Network

https://github.com/romeov/ml4co-competition

공식 코드

Instance-wise algorithm configuration with graph neural networks

https://github.com/romm32/sagnn

공식 코드

Wireless Link Scheduling with State-Augmented Graph Neural Networks

https://github.com/rongrongma/cos-gnn

공식 코드

Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks

https://github.com/rongrongma/mosgnn

공식 코드

Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks

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