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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/dmlc/dgl/tree/master/examples/pytorch/arma

Graph Neural Networks with convolutional ARMA filters

https://github.com/dmlc/dgl/tree/master/examples/pytorch/caregnn

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

https://github.com/dmlc/dgl/tree/master/examples/pytorch/dagnn

Towards Deeper Graph Neural Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/diffpool

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/dmlc/dgl/tree/master/examples/pytorch/gnn_explainer

GNNExplainer: Generating Explanations for Graph Neural Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/hgp_sl

Hierarchical Graph Pooling with Structure Learning

https://github.com/dmlc/dgl/tree/master/examples/pytorch/line_graph

Supervised Community Detection with Line Graph Neural Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/seal

Link Prediction Based on Graph Neural Networks

https://github.com/dodo47/SpikE

Learning through structure: towards deep neuromorphic knowledge graph embeddings

https://github.com/dsulem/dynnet

Graph similarity learning for change-point detection in dynamic networks

https://github.com/edapinenut/cbgbench

Generating 3D Molecules for Target Protein Binding

https://github.com/epfl-imos/htgnn

Graph Neural Networks for Virtual Sensing in Complex Systems: Addressing Heterogeneous Temporal Dynamics

https://github.com/epicosy/devign

Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

https://github.com/facebookresearch/SEAL_OGB

Link Prediction Based on Graph Neural Networks

https://github.com/facebookresearch/ppuda

Graph HyperNetworks for Neural Architecture Search

https://github.com/facebookresearch/taser-tgnn

TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs

https://github.com/farakiko/xai4hep

Explaining machine-learned particle-flow reconstruction

https://github.com/fidler-lab/polyrnn-pp

Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++

https://github.com/fidler-lab/polyrnn-pp-pytorch

Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++

https://github.com/florianeBhz/GraphRec

Graph Neural Networks for Social Recommendation

https://github.com/flyingdoog/PTDNet

Learning to Drop: Robust Graph Neural Network via Topological Denoising

https://github.com/fpour/tgb_baselines

TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

https://github.com/fs302/EasyLink

Link Prediction Based on Graph Neural Networks

https://github.com/fs302/EasyLink/blob/main/example/ogbl_ppa_ra.py

Link Prediction Based on Graph Neural Networks

https://github.com/fumiyo0607/KGAN

KGAT: Knowledge Graph Attention Network for Recommendation

https://github.com/gasteigerjo/ocp

The Open Catalyst 2020 (OC20) Dataset and Community Challenges

https://github.com/gbouritsas/gsn

Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting

https://github.com/gcucurull/visual-compatibility

Context-Aware Visual Compatibility Prediction

https://github.com/giantonia/KGATv2

KGAT: Knowledge Graph Attention Network for Recommendation

https://github.com/gnns4hri/sngnn1dv2

A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation

https://github.com/gnns4hri/socnavgym

A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation

https://github.com/google-deepmind/clrs

A Generalist Neural Algorithmic Learner

https://github.com/gospodima/extended-simgnn

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/gouthamvgk/SuperGlue_training

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/grand-lab/cola

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

https://github.com/graphnet-team/graphnet

Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube

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