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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/hoyunchoi/GNRK

공식 코드

GNRK: Graph Neural Runge-Kutta method for solving partial differential equations

https://github.com/hsipingli/chigad

공식 코드

Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection

https://github.com/htcr/sam_road

공식 코드

Segment Anything Model for Road Network Graph Extraction

https://github.com/huaizepeng2020/m2gnn_git

공식 코드

M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation

https://github.com/huangjunjie95/SiGAT

공식 코드

Signed Graph Attention Networks

https://github.com/huankoh/cst-gl

공식 코드

Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection

https://github.com/hugochan/GraphFlow

공식 코드

GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension

https://github.com/hugochan/RL-based-Graph2Seq-for-NQG

공식 코드

Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation

https://github.com/hugoschnoering2/BTCGraphLabeling

공식 코드

Bitcoin Research with a Transaction Graph Dataset

https://github.com/human-analysis/diverse3dface

공식 코드

Generating Diverse 3D Reconstructions from a Single Occluded Face Image

https://github.com/hxttkl/GraphAdapter

공식 코드

Can GNN be Good Adapter for LLMs?

https://github.com/hyeonahkimm/neuralsep

공식 코드

A Neural Separation Algorithm for the Rounded Capacity Inequalities

https://github.com/hyeonsoojo/metagc

공식 코드

Robust Graph Clustering via Meta Weighting for Noisy Graphs

https://github.com/hysonlab/multiscalehsi

공식 코드

MOB-GCN: A Novel Multiscale Object-Based Graph Neural Network for Hyperspectral Image Classification

https://github.com/hysonlab/tesgraph

공식 코드

TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene Understanding

https://github.com/iarai/neurips2022-traffic4cast

공식 코드

Multi-task Learning for Sparse Traffic Forecasting

https://github.com/ict-gimlab/sehgnn

공식 코드

Simple and Efficient Heterogeneous Graph Neural Network

https://github.com/idc-neu/neutronbench

공식 코드

Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective

https://github.com/idea-iitd/GraphReach

공식 코드

GraphReach: Position-Aware Graph Neural Network using Reachability Estimations

https://github.com/idea-iitd/greed

공식 코드

GREED: A Neural Framework for Learning Graph Distance Functions

https://github.com/idea-iitd/neurocut

공식 코드

NeuroCUT: A Neural Approach for Robust Graph Partitioning

https://github.com/idea-iitd/neurosed

공식 코드

GREED: A Neural Framework for Learning Graph Distance Functions

https://github.com/iit-pavis/diffassemble

공식 코드

DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly

https://github.com/iit-pavis/posernet

공식 코드

PoserNet: Refining Relative Camera Poses Exploiting Object Detections

https://github.com/ilayluz/learning-amg

공식 코드

Learning Algebraic Multigrid Using Graph Neural Networks

https://github.com/ilyaorson/cyberdreamcatcher

공식 코드

An Attentive Graph Agent for Topology-Adaptive Cyber Defence

https://github.com/imatge-upc/CartNet

공식 코드

A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation

https://github.com/imfusiongmbh/vid-vertebra-identification-dataset

공식 코드

Robust vertebra identification using simultaneous node and edge predicting Graph Neural Networks

https://github.com/intellabs/matsciml

공식 코드

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

https://github.com/intelligentgeometry/gegnn

공식 코드

Learning the Geodesic Embedding with Graph Neural Networks

https://github.com/ipadli/grasper

공식 코드

Grasper: A Generalist Pursuer for Pursuit-Evasion Problems

https://github.com/irmvlab/rlsac

공식 코드

RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation

https://github.com/ispras/gnn-aid

공식 코드

Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense

https://github.com/itssmutnuri/gnnvariants

공식 코드

Global Prediction of COVID-19 Variant Emergence Using Dynamics-Informed Graph Neural Networks

https://github.com/itsukifujii/attrgau

공식 코드

Enhancing Attributed Graph Networks with Alignment and Uniformity Constraints for Session-based Recommendation

https://github.com/itu-ai-ml-in-5g-challenge/itu-ml5g-ps-001-parana

공식 코드

QT-Routenet: Improved GNN generalization to larger 5G networks by fine-tuning predictions from queueing theory

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