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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/koustuvsinha/clutrr

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

https://github.com/koustuvsinha/clutrr-baselines

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

https://github.com/koustuvsinha/clutrr-workshop

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

https://github.com/kyriemao/TagGNN-SIGIR

Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach

https://github.com/lars-research/red-gnn

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/lcwy220/Social-Recommendation

Graph Neural Networks for Social Recommendation

https://github.com/linhthi/ts

Graph Neural Networks for Social Recommendation

https://github.com/liubest/HAConvGNN

HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks

https://github.com/louis2889184/gnn_few_shot_cifar100

Few-Shot Learning with Graph Neural Networks

https://github.com/lsj2408/GraphNorm

GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training

https://github.com/lucidrains/equiformer-pytorch

Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

https://github.com/lucidrains/graph-transformer-pytorch

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

https://github.com/luis-mueller/probing-graph-transformers

On the Bottleneck of Graph Neural Networks and its Practical Implications

https://github.com/luost26/3d-generative-sbdd

Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

https://github.com/m-muaz/Cogmen_SLT

COGMEN: COntextualized GNN based Multimodal Emotion recognitioN

https://github.com/m2man/hada

HADA: A Graph-based Amalgamation Framework in Image-text Retrieval

https://github.com/machine-reasoning-ufrgs/GNN-GCP

Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems

https://github.com/machine-reasoning-ufrgs/TSP-GNN

Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP

https://github.com/machine-reasoning-ufrgs/graph-neural-networks

Typed Graph Networks

https://github.com/maragraziani/interpretAI_DigiPath

Hierarchical Graph Representations in Digital Pathology

https://github.com/matlabmtl/Reco-KGNN-LS

Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

https://github.com/matsagad/mres-project

Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem

https://github.com/matwilso/object_collections

Learning to Manipulate Object Collections Using Grounded State Representations

https://github.com/mdeff/paper-cheblienet

ChebLieNet: Invariant Spectral Graph NNs Turned Equivariant by Riemannian Geometry on Lie Groups

https://github.com/mediabrain-sjtu/freealign

Robust Collaborative Perception without External Localization and Clock Devices

https://github.com/meltzerpete/PiNet

PiNet: A Permutation Invariant Graph Neural Network for Graph Classification

https://github.com/mengliu1998/DeeperGNN

Towards Deeper Graph Neural Networks

https://github.com/mickeysjm/taxoexpan

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

https://github.com/microsoft/StemGNN

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

https://github.com/microsoft/causica

Simultaneous Missing Value Imputation and Structure Learning with Groups

https://github.com/microsoft/ptgnn

Typilus: Neural Type Hints

https://github.com/migalkin/NodePiece

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/migalkin/NodePiece/tree/main/ogb

Inductive Relation Prediction by Subgraph Reasoning

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

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