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Privacy Preserving

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코드

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

https://github.com/Pengxin-Guo/HGNN

공식 코드

Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network

https://github.com/Peterbotliang/NEBP-Cooperative-Localization

공식 코드

Neural Enhanced Belief Propagation for Cooperative Localization

https://github.com/PlusRoss/Hy-Transformer

공식 코드

Improving Hyper-Relational Knowledge Graph Completion

https://github.com/Practicing-Federated-Learning-for-IoT/FedGRU

공식 코드

Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach

https://github.com/QTrkX/qtrkx-gnn-tracking

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Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction

https://github.com/QiWang98/PromptCGL

공식 코드

Prompt-Driven Continual Graph Learning

https://github.com/QingruZhang/Thanos

공식 코드

A Biased Graph Neural Network Sampler with Near-Optimal Regret

https://github.com/RAIL-group/RAIL-group-software/tree/main/modules/lsp_gnn

공식 코드

Improving Reliable Navigation under Uncertainty via Predictions Informed by Non-Local Information

https://github.com/RManLuo/CP-GNN

공식 코드

Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model

https://github.com/RManLuo/gfm-rag

공식 코드

GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation

https://github.com/RRRussell/CauGNN/tree/main/MTS_TEGNN-master

공식 코드

Multivariate Time Series Forecasting with Transfer Entropy Graph

https://github.com/RUCAIBox/CRSLab

공식 코드

CRSLab: An Open-Source Toolkit for Building Conversational Recommender System

https://github.com/RUNCSP/RUN-CSP

공식 코드

Graph Neural Networks for Maximum Constraint Satisfaction

https://github.com/RanyaJumah/PP_Speech_Analysis

공식 코드

Emotionless: Privacy-Preserving Speech Analysis for Voice Assistants

https://github.com/RexYing/gnn-model-explainer

공식 코드

GNNExplainer: Generating Explanations for Graph Neural Networks

https://github.com/RingBDStack/HAE

공식 코드

Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks

https://github.com/RingBDStack/KPGNN

공식 코드

Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs

https://github.com/RingBDStack/SUGAR

공식 코드

SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism

https://github.com/RobertoMCA/keyphrase-extraction-with-graph-embeddings

공식 코드

Enhancing Keyphrase Extraction from Long Scientific Documents using Graph Embeddings

https://github.com/RobinLu1209/Geometer

공식 코드

Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation

https://github.com/Rose-STL-Lab/HDR-IL

공식 코드

Deep Imitation Learning for Bimanual Robotic Manipulation

https://github.com/RuihongQiu/GAG

공식 코드

GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation

https://github.com/S1mple-yyds/Fed_Epi_Classifier

공식 코드

Towards the efficacy of federated prediction for epidemics on networks

https://github.com/SLWZwaard/DMT

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Privacy-Preserving Object Detection & Localization Using Distributed Machine Learning: A Case Study of Infant Eyeblink Conditioning

https://github.com/Sachini/niloc

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Neural Inertial Localization

https://github.com/SamuelFountain/FLStore

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FLStore: Efficient Federated Learning Storage for non-training workloads

https://github.com/SamySam0/SynDeidMLM

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Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language Modeling

https://github.com/Scarecrow0/BGNN-SGG

공식 코드

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

https://github.com/SciML-FL/gan-filter

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Robust Federated Learning Against Poisoning Attacks: A GAN-Based Defense Framework

https://github.com/SenticNet/conv-emotion

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DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation

https://github.com/ShaohuiLiu/GNN_OPF_electricity_market

공식 코드

Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions

https://github.com/SharifAbuadbba/split-learning-1D

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Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?

https://github.com/ShenSuanZiZhen/NIID_Person_Detection

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NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data

https://github.com/Sheng-J/DOM-Q-NET

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DOM-Q-NET: Grounded RL on Structured Language

https://github.com/Shuijing725/CrowdNav_Prediction_AttnGraph

공식 코드

Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph

https://github.com/ShunChengWu/3DSSG

공식 코드

SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences

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