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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/zcajiayin/L2D

공식 코드

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

https://github.com/zch65458525/l2t

공식 코드

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Learning

https://github.com/zealscott/synmeter

공식 코드

Systematic Assessment of Tabular Data Synthesis Algorithms

https://github.com/zehao-dong/CktGNN

공식 코드

CktGNN: Circuit Graph Neural Network for Electronic Design Automation

https://github.com/zekarias-tilahun/SelfGNN

공식 코드

Self-supervised Graph Neural Networks without explicit negative sampling

https://github.com/zemingd/hybridpool4mixup

공식 코드

On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing

https://github.com/zeng-shuang/fedld

공식 코드

Tackling Data Heterogeneity in Federated Learning via Loss Decomposition

https://github.com/zequnl/graphex

공식 코드

Graphine: A Dataset for Graph-aware Terminology Definition Generation

https://github.com/zeroqiaoba/gcnet

공식 코드

GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation

https://github.com/zetayue/MXMNet

공식 코드

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures

https://github.com/zezhishao/d2stgnn

공식 코드

Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

https://github.com/zezhishao/step

공식 코드

Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting

https://github.com/zfgao66/mataltmag

공식 코드

AI-accelerated Discovery of Altermagnetic Materials

https://github.com/zfscgy/Amber

공식 코드

Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation

https://github.com/zhang-yingyi/lsrp

공식 코드

LSRP: A Leader-Subordinate Retrieval Framework for Privacy-Preserving Cloud-Device Collaboration

https://github.com/zhangcx19/gpfedrec

공식 코드

GPFedRec: Graph-guided Personalization for Federated Recommendation

https://github.com/zhangcx19/ijcai-23-pfedrec

공식 코드

Dual Personalization on Federated Recommendation

https://github.com/zhangfuyang/Conv-MPN

공식 코드

Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction

https://github.com/zhanglab-aim/emgnn

공식 코드

Explainable Multilayer Graph Neural Network for Cancer Gene Prediction

https://github.com/zhangruochi/pepland

공식 코드

PepLand: a large-scale pre-trained peptide representation model for a comprehensive landscape of both canonical and non-canonical amino acids

https://github.com/zhangxiaochen95/hignn

공식 코드

Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks

https://github.com/zhao-xingyuan/must

공식 코드

Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)

https://github.com/zhaoningyu1996/hm-gnn

공식 코드

Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks

https://github.com/zhaoxy92/GLaRA

공식 코드

GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

https://github.com/zhaoyu-li/nsnet

공식 코드

NSNet: A General Neural Probabilistic Framework for Satisfiability Problems

https://github.com/zheng-yp/decoupleddgnn

공식 코드

Decoupled Graph Neural Networks for Large Dynamic Graphs

https://github.com/zhengdao-chen/GNN4CD

공식 코드

Supervised Community Detection with Line Graph Neural Networks

https://github.com/zhichunguo/Meta-MGNN

공식 코드

Few-Shot Graph Learning for Molecular Property Prediction

https://github.com/zhimengj0326/gradient_rewiring_editing

공식 코드

Gradient Rewiring for Editable Graph Neural Network Training

https://github.com/zhiqiangzhongddu/hc-gnn

공식 코드

Hierarchical Message-Passing Graph Neural Networks

https://github.com/zhixunlee/fairgb

공식 코드

Rethinking Fair Graph Neural Networks from Re-balancing

https://github.com/zhiyongc/Graph_Convolutional_LSTM

공식 코드

Graph Neural Network for Traffic Forecasting: A Survey

https://github.com/zhiyongc/Seattle-Loop-Data

공식 코드

Graph Neural Network for Traffic Forecasting: A Survey

https://github.com/zhonglifr/logs2graph

공식 코드

Graph Neural Networks based Log Anomaly Detection and Explanation

https://github.com/zhongpeixiang/RGNN

공식 코드

EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

https://github.com/zhongyuanzhao/dutybp

공식 코드

Delay-aware Backpressure Routing Using Graph Neural Networks

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