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Heterogeneous Node Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크: 0
평가표: 7
보존된 평가 기록: 66

Node classification in heterogeneous graphs, where nodes and/or edges have multiple types.

코드

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

https://github.com/bepping/non-oversmoothing-gcns

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/berlincho/RGCN-pytorch

Modeling Relational Data with Graph Convolutional Networks

https://github.com/blueberryc/pyGAT

Graph Attention Networks

https://github.com/bowu/GraphSearch

Graph Attention Networks

https://github.com/calciver/Graph-Attention-Networks

Graph Attention Networks

https://github.com/calderkatyal/CPSC483FinalProject

Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks

https://github.com/chalothon/Graph-Convolutional-Networks-for-Relational-Link-Prediction

Modeling Relational Data with Graph Convolutional Networks

https://github.com/clin366/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/cybermonic/cage-4-submission

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/danielegrattarola/keras-gat

Graph Attention Networks

https://github.com/darnbi/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/davidpicard/homm

Graph Attention Networks

https://github.com/deepchem/moleculenet

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dglai/wsdm2022-challenge

Modeling Relational Data with Graph Convolutional Networks

https://github.com/dmeoli/neuro-sat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/rgcn

Modeling Relational Data with Graph Convolutional Networks

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

Graph Attention Networks

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

Heterogeneous Graph Transformer

https://github.com/dmlc/dgl/tree/master/examples/pytorch/rgcn-hetero

Modeling Relational Data with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/rgcn

Modeling Relational Data with Graph Convolutional Networks

https://github.com/ds4dm/sGat

Graph Attention Networks

https://github.com/ds4dm/sparse-gcn

Graph Attention Networks

https://github.com/dzb1998/pyGAT

Graph Attention Networks

https://github.com/emartinezs44/SparkGCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/fanzhenliu/dagad

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/fongyk/graph-attention

Graph Attention Networks

https://github.com/galkampel/HyperNetworks

Graph Attention Networks

https://github.com/gayanku/FDGATII

Graph Attention Networks

https://github.com/gcucurull/jax-gat

Graph Attention Networks

https://github.com/giuseppefutia/link-prediction-code

Graph Attention Networks

https://github.com/giuseppefutia/semi

Modeling Relational Data with Graph Convolutional Networks

https://github.com/gmum/umwpl2021

Graph Attention Networks

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