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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/JetBrains-Research/python-ml-type-hints

Typilus: Neural Type Hints

https://github.com/Jh-SYSU/MolRep

Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction

https://github.com/JiahuiSun/Exp-Graph-WaveNet

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

https://github.com/JieyuZ2/TMN

TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

https://github.com/JockWang/ACKRec

Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View

https://github.com/JonasGeiping/breaching

Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning

https://github.com/JudyYe/zero-shot-gcn

Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs

https://github.com/KaiyuanZh/censor

Differentially Private Federated Learning: A Client Level Perspective

https://github.com/KarhouTam/FL-bench

Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

https://github.com/Kaslanarian/SAGOD

One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks

https://github.com/Kennard123661/gcc

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

https://github.com/KomorebiLHX/Emotion-Recognition-in-Conversations

DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation

https://github.com/Koukyosyumei/NAIST-FedML-Experiments

FOCUS: Dealing with Label Quality Disparity in Federated Learning

https://github.com/KrishnaswamyLab/LearnableScattering

Data-Driven Learning of Geometric Scattering Networks

https://github.com/LARS-research/SANE

Search to aggregate neighborhood for graph neural network

https://github.com/LARS-research/SNAG

Simplifying Architecture Search for Graph Neural Network

https://github.com/LRacoci/permutation-graphml

Graph Neural Networks: A Review of Methods and Applications

https://github.com/LarsHoldijk/RE-ParameterizedExplainerForGraphNeuralNetworks

GNNExplainer: Generating Explanations for Graph Neural Networks

https://github.com/Lieberk/Paddle-FSL-GNN

Few-Shot Learning with Graph Neural Networks

https://github.com/LiheYoung/MiningFSS

Part-aware Prototype Network for Few-shot Semantic Segmentation

https://github.com/Lin-Group-at-UMass/FBGNN-MBE

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures

https://github.com/LinJayan/SIGN-Paddle

Detecting Beneficial Feature Interactions for Recommender Systems

https://github.com/Linardos/federated-HCM-diagnosis

Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease

https://github.com/Lior157/ComGraphRec

Graph Neural Networks for Social Recommendation

https://github.com/LucaHermes/lightweight-motion-forecasting

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

https://github.com/LunaBlack/KGAT-pytorch

KGAT: Knowledge Graph Attention Network for Recommendation

https://github.com/M-Lampert/DyGLib

TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

https://github.com/MARMOTatZJU/ZSLPR-TIANCHI

Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs

https://github.com/Mars-tin/CopulaGNN-Synthetic

CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks

https://github.com/MattJud/gnn_jssp

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

https://github.com/Microsoft/graph-based-code-modelling

Open Vocabulary Learning on Source Code with a Graph-Structured Cache

https://github.com/Mind23-2/MindCode-2/tree/main/ViG

ViG-UNet: Vision Graph Neural Networks for Medical Image Segmentation

https://github.com/Mind23-2/MindCode-4/tree/main/bgcf

Towards Representation Alignment and Uniformity in Collaborative Filtering

https://github.com/MindCode-4/code-3/tree/main/falcon

FALCON: Feature-Label Constrained Graph Net Collapse for Memory Efficient GNNs

https://github.com/MindSpore-paper-code-3/code2/tree/main/dcrnn

Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting

https://github.com/MindSpore-paper-code-3/code9/tree/main/dgu

Constructing Temporal Dynamic Knowledge Graphs from Interactive Text-based Games

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