Papers with Code 아카이브

Graph Neural Network

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

1

보존된 평가 기록

1

코드

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

https://github.com/graphpku/2wl_link_pred

How Powerful are Spectral Graph Neural Networks

https://github.com/gsl-benchmark/gslb

Hierarchical Graph Pooling with Structure Learning

https://github.com/guanjq/targetdiff

Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

https://github.com/hazdzz/gfNN

Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

https://github.com/hci-unihd/celltype-graph-benchmark

CellTypeGraph: A New Geometric Computer Vision Benchmark

https://github.com/henry1iu/tnt-trajectory-prediction

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation

https://github.com/histocartography/histocartography

Hierarchical Graph Representations in Digital Pathology

https://github.com/hkuds/recdiff

Graph Neural Networks in Recommender Systems: A Survey

https://github.com/huggingface/transformers

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/hustwutao/Deep-Clustering

Spectral Clustering with Graph Neural Networks for Graph Pooling

https://github.com/hwwang55/KGNN-LS

Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

https://github.com/hyperplane-lab/Generative-3D-Part-Assembly

Generative 3D Part Assembly via Dynamic Graph Learning

https://github.com/ibm/materials

MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network

https://github.com/imcbrearty/genie

Earthquake Phase Association with Graph Neural Networks

https://github.com/ipolharvard/readmit-stgnn

GNNExplainer: Generating Explanations for Graph Neural Networks

https://github.com/iqua/m-dgt

Learning Cross-modal Context Graph for Visual Grounding

https://github.com/ivanstepanovftw/glam

A Graphical Approach to Document Layout Analysis

https://github.com/jaemuzzin/plagnn

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/jbr-ai-labs/lipophilicity-prediction

Optimal Transport Graph Neural Networks

https://github.com/jennyzhang0215/DGL-KGAT

KGAT: Knowledge Graph Attention Network for Recommendation

https://github.com/jeonjaehyeong/dpl

Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation

https://github.com/jingsonglv/PSG

Path-aware Siamese Graph Neural Network for Link Prediction

https://github.com/jmpu/deepfaketextdetection

Neural Deepfake Detection with Factual Structure of Text

https://github.com/joanbruna/GNN_community

Supervised Community Detection with Line Graph Neural Networks

https://github.com/joernio/joernti-codetidal5

Advanced Graph-Based Deep Learning for Probabilistic Type Inference

https://github.com/joeybose/Meta-Graph

Meta-Graph: Few Shot Link Prediction via Meta Learning

https://github.com/jolibrain/wheatley

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

https://github.com/jomariya23156/SuperGlue-for-Visual-Place-Recognition

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/jongyaoY/fignet

Learning rigid dynamics with face interaction graph networks

https://github.com/josegg05/eRGWnet

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

https://github.com/junwenbai/correlation-gnn

Residual Correlation in Graph Neural Network Regression

https://github.com/jwwthu/DL4Traffic

Graph Neural Network for Traffic Forecasting: A Survey

https://github.com/jwzhanggy/GResNet

GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation

https://github.com/jwzhanggy/SEG-BERT

Graph Neural Distance Metric Learning with Graph-Bert

https://github.com/kdd-submitter/on_local_aggregation

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

https://github.com/koheishimamura/nequip_allegro_tc

Spin-Dependent Graph Neural Network Potential for Magnetic Materials

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