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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/bozdaglab/graf

공식 코드

GRAF: Graph Attention-aware Fusion Networks

https://github.com/br0kej/kyn

공식 코드

Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets

https://github.com/brianlsy98/dice

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DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining

https://github.com/briansidp/espnet

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Graph Neural Networks for Contextual ASR with the Tree-Constrained Pointer Generator

https://github.com/brozosc/Predicting-the-Temperature-Dependent-CMC-of-Surfactant-Mixtures-with-Graph-Neural-Networks

공식 코드

Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks

https://github.com/buaabigscity/kddcup2022

공식 코드

BUAA_BIGSCity: Spatial-Temporal Graph Neural Network for Wind Power Forecasting in Baidu KDD CUP 2022

https://github.com/buildingnet/buildingnet_dataset

공식 코드

BuildingNet: Learning to Label 3D Buildings

https://github.com/bupt-gamma/space4hgnn

공식 코드

Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network

https://github.com/bytedance/byteff

공식 코드

Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage

https://github.com/c-lyu/traffic4cast2022-tse

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Similarity-based Feature Extraction for Large-scale Sparse Traffic Forecasting

https://github.com/caganselim/tltm

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Two-Level Temporal Relation Model for Online Video Instance Segmentation

https://github.com/camlab-ethz/rigno

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RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains

https://github.com/carocciluca/gnn-site-selection

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Optimizing Luxury Vehicle Dealership Networks: A Graph Neural Network Approach to Site Selection

https://github.com/carrierlxk/AGNN

공식 코드

Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks

https://github.com/cedricvincentcuaz/TFGW

공식 코드

Template based Graph Neural Network with Optimal Transport Distances

https://github.com/cgoliver/rnaglib

공식 코드

A Comprehensive Benchmark for RNA 3D Structure-Function Modeling

https://github.com/chaitjo/geometric-rna-design

공식 코드

gRNAde: Geometric Deep Learning for 3D RNA inverse design

https://github.com/changminwu/expandergnn

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Sparsifying the Update Step in Graph Neural Networks

https://github.com/chao1224/n_gram_graph

공식 코드

N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules

https://github.com/chao1224/sgnn-ebm

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Structured Multi-task Learning for Molecular Property Prediction

https://github.com/chaoyanghe/bipartite-graph-learning

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Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs

https://github.com/cheikh025/DistrictNet

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DistrictNet: Decision-aware learning for geographical districting

https://github.com/chenfengzhao/gnnhls

공식 코드

GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis

https://github.com/chengzhag/DeepPanoContext

공식 코드

DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization

https://github.com/chengzhag/Implicit3DUnderstanding

공식 코드

Holistic 3D Scene Understanding from a Single Image with Implicit Representation

https://github.com/chenjie20/SSCDL

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Conditional Distribution Learning on Graphs

https://github.com/chenzhaiyu/polygnn

공식 코드

PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds

https://github.com/chenzrg/behrmecom

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BernGraph: Probabilistic Graph Neural Networks for EHR-based Medication Recommendations

https://github.com/chiang-yuan/prodar

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Encoding protein dynamic information in graph representation for functional residue identification

https://github.com/chinmay5/ms_ida

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Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images

https://github.com/chisam0217/Graph-Universal-Attack

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Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models

https://github.com/choderalab/espaloma_charge

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EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment

https://github.com/chrisbloecker/neuromap

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The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks

https://github.com/chrisdxie/rice

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RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks

https://github.com/chrisjtan/gnn_cff

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Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning

https://github.com/chromaprim/HybowaveNet

공식 코드

HyboWaveNet: Hyperbolic Graph Neural Networks with Multi-Scale Wavelet Transform for Protein-Protein Interaction Prediction

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