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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/Xianjie-Guo/FedPC

공식 코드

Towards Privacy-Aware Causal Structure Learning in Federated Setting

https://github.com/XiaoxinHe/TAPE

공식 코드

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

https://github.com/XieResearchGroup/Physics-aware-Multiplex-GNN

공식 코드

Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes

https://github.com/Xovee/hdgnn

공식 코드

A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact

https://github.com/Xuanmeng-Zhang/gnn-re-ranking

공식 코드

Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective

https://github.com/YLTun/federated_diffusion_model

공식 코드

Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks

https://github.com/YangLiangwei/game-recommendation

공식 코드

Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network

https://github.com/Yanzongzi/GNN-For-localization

공식 코드

Graph Neural Network for Large-Scale Network Localization

https://github.com/YingtongDou/CARE-GNN

공식 코드

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

https://github.com/YuWVandy/TDGNN

공식 코드

Tree Decomposed Graph Neural Network

https://github.com/Yuma-Ichikawa/CRA4CO

공식 코드

Controlling Continuous Relaxation for Combinatorial Optimization

https://github.com/YuxiangRen/Label-Contrastive-Coding-based-Graph-Neural-Network-for-Graph-Classification-

공식 코드

Label Contrastive Coding based Graph Neural Network for Graph Classification

https://github.com/ZM7/DGSR

공식 코드

Dynamic Graph Neural Networks for Sequential Recommendation

https://github.com/ZeXingZ/DIG-Mol

공식 코드

Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction

https://github.com/Zening-Li/PrivGE

공식 코드

Privacy-Preserving Graph Embedding based on Local Differential Privacy

https://github.com/Zhangcx19/IJCAI-24-FedPA

공식 코드

Federated Adaptation for Foundation Model-based Recommendations

https://github.com/Zhanyu-Wang/Differentially_Private_Bootstrap

공식 코드

Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies

https://github.com/ZhenanFanUBC/FairVFL.jl

공식 코드

Achieving Model Fairness in Vertical Federated Learning

https://github.com/ZhenhHuang/MSG

공식 코드

Spiking Graph Neural Network on Riemannian Manifolds

https://github.com/ZhenyueQin/Angular-Skeleton-Encoding

공식 코드

Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition

https://github.com/Zhiyu014/GNN-UDS

공식 코드

Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks

https://github.com/ZhuYun97/ENGINE

공식 코드

Efficient Tuning and Inference for Large Language Models on Textual Graphs

https://github.com/Zi-YuanYang/FedPalm

공식 코드

FedPalm: A General Federated Learning Framework for Closed- and Open-Set Palmprint Verification

https://github.com/ZihanChen1995/ChatGPT-GNN-StockPredict

공식 코드

ChatGPT Informed Graph Neural Network for Stock Movement Prediction

https://github.com/ZijieH/LG-ODE

공식 코드

Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations

https://github.com/a791702141/SSG

공식 코드

Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation

https://github.com/aaltovision/dgc-gnn-release

공식 코드

DGC-GNN: Leveraging Geometry and Color Cues for Visual Descriptor-Free 2D-3D Matching

https://github.com/aaronge-2020/sig3-detection

공식 코드

A FAIR platform for reproducing mutational signature detection on tumor sequencing data

https://github.com/aaronzo/STAGE

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STAGE: Simplified Text-Attributed Graph Embeddings Using Pre-trained LLMs

https://github.com/abbahaddou/CGSO

공식 코드

Centrality Graph Shift Operators for Graph Neural Networks

https://github.com/abbahaddou/DGOW

공식 코드

Graph Neural Networks on Discriminative Graphs of Words

https://github.com/abbottyanginchina/awesome-federated-unlearning

공식 코드

Exploring Federated Unlearning: Review, Comparison, and Insights

https://github.com/abedicodes/fedsl

공식 코드

FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks

https://github.com/abhaysobhanan/gancp

공식 코드

Genetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems

https://github.com/abheesht17/super-pixels

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Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification

https://github.com/abhitall/federated-credit-risk-rtfl

공식 코드

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries

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