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Graph Neural Network

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코드

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

https://bitbucket.org/aauvap/ctgnn

공식 코드

Multi-Task Classification of Sewer Pipe Defects and Properties using a Cross-Task Graph Neural Network Decoder

https://git.rwth-aachen.de/netsci/publication-scale-dependent-node-roles

공식 코드

Local, global and scale-dependent node roles

https://github.com/000Justin000/GBPN

공식 코드

Graph Belief Propagation Networks

https://github.com/000Justin000/gnn-residual-correlation

공식 코드

Residual Correlation in Graph Neural Network Regression

https://github.com/0215arthur/hg-gnn

공식 코드

Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

https://github.com/1041877801/COMBHelper

공식 코드

COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems

https://github.com/2024sig/sig

공식 코드

SIG: Efficient Self-Interpretable Graph Neural Network for Continuous-time Dynamic Graphs

https://github.com/3dem/model-angelo

공식 코드

A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps

https://github.com/3dlg-hcvc/plan2scene

공식 코드

Plan2Scene: Converting Floorplans to 3D Scenes

https://github.com/3dperceptionlab/biotacsp-stability-set-v2

공식 코드

TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors

https://github.com/564612540/GLASU

공식 코드

GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data

https://github.com/77yq77/scbit

공식 코드

scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis

https://github.com/AIforGreatGood/charge3net

공식 코드

Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials

https://github.com/AIoT-Lab-BKAI/PIMA

공식 코드

A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning

https://github.com/AaltoPML/Rethinking-pooling-in-GNNs

공식 코드

Rethinking pooling in graph neural networks

https://github.com/Abrar2652/Network-Intrusion-Detection

공식 코드

CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion Detection

https://github.com/Academic-Hammer/SciTSR

공식 코드

Complicated Table Structure Recognition

https://github.com/Acciorocketships/pymarl2

공식 코드

QGNN: Value Function Factorisation with Graph Neural Networks

https://github.com/Alex-Mathai-98/Monolith-to-Microservices

공식 코드

Monolith to Microservices: Representing Application Software through Heterogeneous Graph Neural Network

https://github.com/AliSaadatV/Causal-Gene-Pathway-Finder

공식 코드

DNA Language Model and Interpretable Graph Neural Network Identify Genes and Pathways Involved in Rare Diseases

https://github.com/AmitRoy7781/SST-GNN

공식 코드

SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network

https://github.com/AmitRoy7781/USTGCN

공식 코드

Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network

https://github.com/AndreasOpedal/ba-graph-disentanglement

공식 코드

Recovering Barabási-Albert Parameters of Graphs through Disentanglement

https://github.com/AnimeshSinha1309/qroute-router

공식 코드

Qubit Routing using Graph Neural Network aided Monte Carlo Tree Search

https://github.com/AnsongLi/Disen-GNN

공식 코드

Disentangled Graph Neural Networks for Session-based Recommendation

https://github.com/AnsongLi/TIE-DGNN

공식 코드

Transition Information Enhanced Disentangled Graph Neural Networks for Session-based Recommendation

https://github.com/AsalMehradfar/FALCON

공식 코드

FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design

https://github.com/Autumn945/MGNN-SPred

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Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

https://github.com/B-Deprez/BeGin

공식 코드

Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review

https://github.com/BNN-UPC/ENERO

공식 코드

ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning

https://github.com/BNN-UPC/GNN-NIDS

공식 코드

Unveiling the potential of Graph Neural Networks for robust Intrusion Detection

https://github.com/BUAABIGSCity/PDFormer

공식 코드

PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction

https://github.com/BUPT-GAMMA/OpenHGNN

공식 코드

Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks

https://github.com/BUserName/PGL

공식 코드

Progressive Graph Learning for Open-Set Domain Adaptation

https://github.com/BangLiu/GIANT

공식 코드

GIANT: Scalable Creation of a Web-scale Ontology

https://github.com/BaratiLab/OFormer

공식 코드

Transformer for Partial Differential Equations' Operator Learning

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