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Node Property Prediction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

5

보존된 평가 기록

235

코드

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

https://github.com/Azadeh297/BiGTex

공식 코드

Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTex

https://github.com/CUAI/CorrectAndSmooth

공식 코드

Combining Label Propagation and Simple Models Out-performs Graph Neural Networks

https://github.com/CrawlScript/RpHGNN

공식 코드

Efficient Heterogeneous Graph Learning via Random Projection

https://github.com/GISec-Team/GNNMoE

공식 코드

Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification

https://github.com/GraphSAINT/GraphSAINT

공식 코드

GraphSAINT: Graph Sampling Based Inductive Learning Method

https://github.com/JeanKaddour/WASAM

공식 코드

When Do Flat Minima Optimizers Work?

https://github.com/LUOyk1999/tunedGNN

공식 코드

Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

https://github.com/LWHYC/RPR-Loc

공식 코드

Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images

https://github.com/PaddlePaddle/PGL/tree/main/ogb_examples/nodeproppred/unimp

공식 코드

Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification

https://github.com/PetarV-/GAT

공식 코드

Graph Attention Networks

https://github.com/SeanChenxy/HandMesh

공식 코드

MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Image

https://github.com/Tiiiger/SGC

공식 코드

Simplifying Graph Convolutional Networks

https://github.com/UlrikFriisJensen/CHILI

공식 코드

CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning

https://github.com/XiaoxinHe/TAPE

공식 코드

Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

https://github.com/abotond/sd-layernet

공식 코드

SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors

https://github.com/acbull/pyHGT

공식 코드

Heterogeneous Graph Transformer

https://github.com/aditya-grover/node2vec

공식 코드

node2vec: Scalable Feature Learning for Networks

https://github.com/amzn/pecos

공식 코드

Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction

https://github.com/andyjzhao/glem

공식 코드

Learning on Large-scale Text-attributed Graphs via Variational Inference

https://github.com/benfinkelshtein/equivarianceeverywhere

공식 코드

Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models

https://github.com/cf020031308/LinkDist

공식 코드

Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages

https://github.com/chennnM/GCNII

공식 코드

Simple and Deep Graph Convolutional Networks

https://github.com/cornell-zhang/polynormer

공식 코드

Polynormer: Polynomial-Expressive Graph Transformer in Linear Time

https://github.com/devnkong/FLAG

공식 코드

Robust Optimization as Data Augmentation for Large-scale Graphs

https://github.com/divelab/DeeperGNN

공식 코드

Towards Deeper Graph Neural Networks

https://github.com/espylapiza/Bag-of-Tricks-for-Node-Classification-with-Graph-Neural-Networks

공식 코드

Bag of Tricks for Node Classification with Graph Neural Networks

https://github.com/facebookresearch/NARS

공식 코드

Scalable Graph Neural Networks for Heterogeneous Graphs

https://github.com/facebookresearch/denoised_mdp

공식 코드

Denoised MDPs: Learning World Models Better Than the World Itself

https://github.com/facebookresearch/shaDow_GNN

공식 코드

Decoupling the Depth and Scope of Graph Neural Networks

https://github.com/fpour/tgb_baselines

공식 코드

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

https://github.com/google-research/google-research

공식 코드

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

https://github.com/houyili/gipa_wide_deep

공식 코드

GIPA: A General Information Propagation Algorithm for Graph Learning

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

공식 코드

Simple and Efficient Heterogeneous Graph Neural Network

https://github.com/ivam-he/pshgcn

공식 코드

Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials

https://github.com/jhl-hust/ldmlp

공식 코드

Long-range Meta-path Search on Large-scale Heterogeneous Graphs

https://github.com/jhl-hust/lmsps

공식 코드

Long-range Meta-path Search on Large-scale Heterogeneous Graphs

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