Papers with Code 아카이브

Privacy Preserving

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코드

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

https://github.com/florianeBhz/GraphRec

Graph Neural Networks for Social Recommendation

https://github.com/flyingdoog/PTDNet

Learning to Drop: Robust Graph Neural Network via Topological Denoising

https://github.com/fpour/tgb_baselines

TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

https://github.com/fs302/EasyLink

Link Prediction Based on Graph Neural Networks

https://github.com/fs302/EasyLink/blob/main/example/ogbl_ppa_ra.py

Link Prediction Based on Graph Neural Networks

https://github.com/fumiyo0607/KGAN

KGAT: Knowledge Graph Attention Network for Recommendation

https://github.com/gabbygab1233/Image-Anonymization

Learning to Anonymize Faces for Privacy Preserving Action Detection

https://github.com/gaow0007/ATSPrivacy

Privacy-preserving Collaborative Learning with Automatic Transformation Search

https://github.com/gasteigerjo/ocp

The Open Catalyst 2020 (OC20) Dataset and Community Challenges

https://github.com/gbouritsas/gsn

Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting

https://github.com/gcucurull/visual-compatibility

Context-Aware Visual Compatibility Prediction

https://github.com/giantonia/KGATv2

KGAT: Knowledge Graph Attention Network for Recommendation

https://github.com/giovannt0/dpgbdt

Privacy-Preserving Gradient Boosting Decision Trees

https://github.com/gnns4hri/sngnn1dv2

A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation

https://github.com/gnns4hri/socnavgym

A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation

https://github.com/google-deepmind/clrs

A Generalist Neural Algorithmic Learner

https://github.com/google-deepmind/jax_privacy

Unlocking Accuracy and Fairness in Differentially Private Image Classification

https://github.com/gospodima/extended-simgnn

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/gouthamvgk/SuperGlue_training

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/grand-lab/cola

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

https://github.com/graphnet-team/graphnet

Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube

https://github.com/graphpku/2wl_link_pred

How Powerful are Spectral Graph Neural Networks

https://github.com/gsl-benchmark/gslb

Hierarchical Graph Pooling with Structure Learning

https://github.com/guanjq/targetdiff

Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

https://github.com/hafizimtiaz/cape

Distributed Differentially Private Computation of Functions with Correlated Noise

https://github.com/hafizimtiaz/capeFM

Distributed Differentially Private Computation of Functions with Correlated Noise

https://github.com/hazdzz/gfNN

Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

https://github.com/hci-unihd/celltype-graph-benchmark

CellTypeGraph: A New Geometric Computer Vision Benchmark

https://github.com/henry1iu/tnt-trajectory-prediction

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation

https://github.com/hepengli/matrpo

Multi-Agent Trust Region Policy Optimization

https://github.com/histocartography/histocartography

Hierarchical Graph Representations in Digital Pathology

https://github.com/hkuds/recdiff

Graph Neural Networks in Recommender Systems: A Survey

https://github.com/hpdic/pfllib

GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning

https://github.com/huggingface/transformers

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/hustwutao/Deep-Clustering

Spectral Clustering with Graph Neural Networks for Graph Pooling

https://github.com/hwang595/ATOMO

Fast Federated Learning by Balancing Communication Trade-Offs

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