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Graph Neural Network

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

https://github.com/FujitsuLaboratories/bermudatriangles

공식 코드

Bermuda Triangles: GNNs Fail to Detect Simple Topological Structures

https://github.com/GNN-based-MIMO-Detection/GNN-based-MIMO-Detection

공식 코드

Graph Neural Network Aided MU-MIMO Detectors

https://github.com/GemsLab/H2GCN

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

https://github.com/Graph-Machine-Learning-Group/grin

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Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks

https://github.com/HammerLabML/SPIGNNs_for_WDSs

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Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems

https://github.com/HaoZhongkai/AS_Molecule

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ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction

https://github.com/HazeDT/ARGWAE

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A Novel Unsupervised Graph Wavelet Autoencoder for Mechanical System Fault Detection

https://github.com/HekpoMaH/algorithmic-concepts-reasoning

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Algorithmic Concept-based Explainable Reasoning

https://github.com/IBM/IPC-graph-data

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Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

https://github.com/IIT-PAVIS/Positional_Diffusion

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Positional Diffusion: Ordering Unordered Sets with Diffusion Probabilistic Models

https://github.com/Ivanopolo/modnet

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Unsupervised Community Detection with Modularity-Based Attention Model

https://github.com/JRowbottomGit/g-adaptivity

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G-Adaptivity: optimised graph-based mesh relocation for finite element methods

https://github.com/JasonZhujp/RELIEF

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RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning

https://github.com/JetRichardLee/Attack-XGNN

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Graph Neural Network Explanations are Fragile

https://github.com/Jiaolong/gcn-parking-slot

공식 코드

Attentional Graph Neural Network for Parking-slot Detection

https://github.com/JiaxuanYou/P-GNN

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Position-aware Graph Neural Networks

https://github.com/JimLiu96/MITGNN

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Basket Recommendation with Multi-Intent Translation Graph Neural Network

https://github.com/JinyuZ1996/RL-ISN

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Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation

https://github.com/JinyuZ1996/TiDA-GCN

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Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation

https://github.com/JiuhaiChen/EBBS

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Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features

https://github.com/Junyoungpark/CGS

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Convergent Graph Solvers

https://github.com/K-Wu/pytorch-direct_dgl

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PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses

https://github.com/Kaimaoge/IGNNK

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Inductive Graph Neural Networks for Spatiotemporal Kriging

https://github.com/KnowledgeDiscovery/StrGNN

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Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

https://github.com/LARS-research/HL-GNN

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Heuristic Learning with Graph Neural Networks: A Unified Framework for Link Prediction

https://github.com/LIONS-EPFL/dynamic-MIS

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Maximum Independent Set: Self-Training through Dynamic Programming

https://github.com/LLNL/FAST

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Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference

https://github.com/LTS4/gnn_statistics

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On the choice of graph neural network architectures

https://github.com/LUMII-Syslab/Matrix-SE

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Matrix Shuffle-Exchange Networks for Hard 2D Tasks

https://github.com/LUMII-Syslab/Switchblade

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Matrix Shuffle-Exchange Networks for Hard 2D Tasks

https://github.com/LUOyk1999/NodeID

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Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning

https://github.com/LeiBAI/AGCRN

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Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

https://github.com/Lhongpei/ipps-drl

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Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning

https://github.com/LiXinran6/LSDGNN_ICL

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Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation

https://github.com/LifengFan/Human-Gaze-Communication

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Understanding Human Gaze Communication by Spatio-Temporal Graph Reasoning

https://github.com/Lihaogx/DNDN

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Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs

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