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Privacy Preserving

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

https://github.com/CurryTang/Graph-LLM

Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

https://github.com/Cynwell/Text-Level-GNN

Text Level Graph Neural Network for Text Classification

https://github.com/DM2-ND/CoEvoGNN

Learning Attribute-Structure Co-Evolutions in Dynamic Graphs

https://github.com/DPBayes/twinify

Privacy-preserving data sharing via probabilistic modelling

https://github.com/DQSSSSS/VectorNet

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation

https://github.com/DeepGraphLearning/FewShotRE

Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

https://github.com/DeepGraphLearning/GMNN

GMNN: Graph Markov Neural Networks

https://github.com/DenseAI/awesome-biomedical-machine-learning

Graph Neural Networks: A Review of Methods and Applications

https://github.com/DenseAI/awesome-medical-machine-learning

Graph Neural Networks: A Review of Methods and Applications

https://github.com/E666GT/TrafficPredictionNN

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

https://github.com/ElliottKasoar/GeneVAE

DAG-GNN: DAG Structure Learning with Graph Neural Networks

https://github.com/ElliottKasoar/gene-dag-vae

DAG-GNN: DAG Structure Learning with Graph Neural Networks

https://github.com/ErickaBermudez/har-fl-simulation

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

https://github.com/Forrest-Stone/MA-GNN

Memory Augmented Graph Neural Networks for Sequential Recommendation

https://github.com/GNNAsKernel/GNNAsKernel

From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness

https://github.com/GeminiLight/virne

Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning

https://github.com/George730/E-ResGAT

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT

https://github.com/GitEventhandler/H2GCN-PyTorch

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

https://github.com/Golbstein/GlueStick-tensorflow/blob/main/gluestick.py

GlueStick: Robust Image Matching by Sticking Points and Lines Together

https://github.com/Graph-COM/GSAT

Parameterized Explainer for Graph Neural Network

https://github.com/Guang000/Awesome-Dataset-Distillation

Privacy for Free: How does Dataset Condensation Help Privacy?

https://github.com/HanXudong/ReImplementation_Robust_and_Privacy_preserving_Text_Representations

Towards Robust and Privacy-preserving Text Representations

https://github.com/Hanjun-Dai/graph_adversarial_attack

Adversarial Attack on Graph Structured Data

https://github.com/HeapHop30/hierarchical-pooling

Towards Sparse Hierarchical Graph Classifiers

https://github.com/HeatherJiaZG/SuperGlue-pytorch

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/HoganZhang/few-shot-gnn

Few-Shot Learning with Graph Neural Networks

https://github.com/IBM/LogicalFormulaEmbedder

Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling

https://github.com/IBM/MMMG

Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis

https://github.com/ICT-GIMLab/SeHGNN/tree/master/large

Simple and Efficient Heterogeneous Graph Neural Network

https://github.com/INDUCE-Lab/HealthEdge-Diabetes-Prediction

Secure and Privacy-Preserving Automated Machine Learning Operations into End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction

https://github.com/IcurasLW/MTSFormer-Irregular_Time_Series

Graph-Guided Network for Irregularly Sampled Multivariate Time Series

https://github.com/IvanIsCoding/GNN-for-Combinatorial-Optimization

Combinatorial Optimization with Physics-Inspired Graph Neural Networks

https://github.com/JIAQING-XIE/Fea2Fea

Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks

https://github.com/Jaroan/Fair-MARL

Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation

https://github.com/Jaskiee/GAN-Attack-against-Federated-Deep-Learning

Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

https://github.com/JeeseongKim/SuperGlue

SuperGlue: Learning Feature Matching with Graph Neural Networks

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