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/hwwang55/KGNN-LS

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

https://github.com/hyperplane-lab/Generative-3D-Part-Assembly

Generative 3D Part Assembly via Dynamic Graph Learning

https://github.com/ibm/materials

MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network

https://github.com/imcbrearty/genie

Earthquake Phase Association with Graph Neural Networks

https://github.com/ipolharvard/readmit-stgnn

GNNExplainer: Generating Explanations for Graph Neural Networks

https://github.com/iqua/m-dgt

Learning Cross-modal Context Graph for Visual Grounding

https://github.com/ivanstepanovftw/glam

A Graphical Approach to Document Layout Analysis

https://github.com/jaemuzzin/plagnn

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/jbr-ai-labs/lipophilicity-prediction

Optimal Transport Graph Neural Networks

https://github.com/jeffreysijuntan/cryptgpu

CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU

https://github.com/jennyzhang0215/DGL-KGAT

KGAT: Knowledge Graph Attention Network for Recommendation

https://github.com/jeonjaehyeong/dpl

Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation

https://github.com/jingsonglv/PSG

Path-aware Siamese Graph Neural Network for Link Prediction

https://github.com/jmpu/deepfaketextdetection

Neural Deepfake Detection with Factual Structure of Text

https://github.com/joanbruna/GNN_community

Supervised Community Detection with Line Graph Neural Networks

https://github.com/joernio/joernti-codetidal5

Advanced Graph-Based Deep Learning for Probabilistic Type Inference

https://github.com/joeybose/Meta-Graph

Meta-Graph: Few Shot Link Prediction via Meta Learning

https://github.com/jolibrain/wheatley

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

https://github.com/jomariya23156/SuperGlue-for-Visual-Place-Recognition

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/jongyaoY/fignet

Learning rigid dynamics with face interaction graph networks

https://github.com/josegg05/eRGWnet

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

https://github.com/junwenbai/correlation-gnn

Residual Correlation in Graph Neural Network Regression

https://github.com/jwwthu/DL4Traffic

Graph Neural Network for Traffic Forecasting: A Survey

https://github.com/jwzhanggy/GResNet

GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation

https://github.com/jwzhanggy/SEG-BERT

Graph Neural Distance Metric Learning with Graph-Bert

https://github.com/kai-yue/predictive-coding-fl

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

https://github.com/kdd-submitter/on_local_aggregation

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs

https://github.com/kenctrl/Differential-Privacy-2019

Differentially Private M-band Wavelet-Based Mechanisms in Machine Learning Environments

https://github.com/koheishimamura/nequip_allegro_tc

Spin-Dependent Graph Neural Network Potential for Magnetic Materials

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/krishnap25/trfa

Robust Aggregation for Federated Learning

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

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